Latent Dirichlet Allocation (LDA) is one of the most popular algorithms for Topic Modeling, i.e. having a computer find out what a text is about. LDA is also perhaps easier to understand than the other popular Topic Modeling approach, (P)LSA, but even though there are two well-written blog posts that explain LDA (Edwin Chen’s and Ted Underwood’s) to non-mathematicians, it still took me quite some time to grasp LDA well enough to be able to code it in a Perl script (which I have made available on GitHub, in case anyone is interested). Of course, you can always simply use a software like Mallet that runs LDA over your documents and outputs the results, but if you want to know what LDA actually does, I suggest you read Edwin Chen’s and Ted Underwood’s blog posts first, and then, if you still feel you don’t really get LDA, come back here. OK?
Welcome back. Disclaimer: I’m not a mathematician and there’s still the possibility that I got it all wrong. That being said, let’s take a look at Edwin Chen’s first example again, and this time we’re going to calculate it through step by step:
- I like to eat broccoli and bananas.
- I ate a banana and spinach smoothie for breakfast.
- Chinchillas and kittens are cute.
- My sister adopted a kitten yesterday.
- Look at this cute hamster munching on a piece of broccoli.
We immediately see that these sentences are about either eating or pets or both, but even if we didn’t know about these two topics, we still have to make an assumption about the number of topics within our corpus of documents. Furthermore, we have to make an assumption how these topics are distributed over the corpus. (In real life LDA analyses, you’d run the algorithm multiple times with different parameters and then see which fit best.) For simplicity’s sake, let’s assume there are 2 topics, which we’ll call A and B, and they’re distributed evenly: half of the words in the corpus belong to topic A and the other half to topic B.
What exactly is a word, though? I found the use of this term confusing in both Chen’s and Underwood’s text, so instead I’ll speak of tokens and lemmata: the lemma ‘cute’ appears as 2 tokens in the corpus above. Before we apply the actual LDA algorithm, it makes sense to not only tokenise but also lemmatise our 5 example documents (i.e. sentences), and also to remove stop words such as pronouns and prepositions, which may result in something like this:
- like eat broccoli banana
- eat banana spinach smoothie breakfast
- chinchilla kitten cute
- sister adopt kitten yesterday
- look cute hamster munch piece broccoli
Now we randomly assign topics to tokens according to our assumptions (2 topics, 50:50 distribution). This may result in e.g. ‘cute’ getting assigned once to topic A and once to topic B. An initial random topic assignment may look like this:
- like -> A, eat -> B, broccoli -> A, banana -> B
- eat -> A, banana -> B, spinach -> A, smoothie -> B, breakfast -> A
- chinchilla -> B, kitten -> A, cute -> B
- sister -> A, adopt -> B, kitten -> A, yesterday -> B
- look -> A, cute -> B, hamster -> A, munch -> B, piece -> A, broccoli -> B
Clearly, this isn’t a satisfying result yet; words like ‘eat’ and ‘broccoli’ are assigned to multiple topics when they should belong to only one, etc. Ideally, all words connected to the topic of eating should be assigned to one topic and all words related to pets should belong to the other. Now the LDA algorithm goes through the documents to improve this initial topic assignment: it computes probabilities which topic each token should belong to, based on three criteria:
- Which topics are the other tokens in this document assigned to? Probably the document is about one single topic, so if all or most other tokens belong to topic A, then the token in question should most likely also get assigned to topic A.
- Which topics are the other tokens in *all* documents assigned to? Remember that we assume a 50:50 distribution of topics, so if the majority of tokens is assigned to topic A, the token in question should get assigned to topic B to establish an equilibrium.
- If there are multiple tokens of the same lemma: which topic is the majority of tokens of that lemma assigned to? If most instances of ‘eat’ belong to topic A, then the token in question probably also belongs to topic A.
The actual formulas to calculate the probabilities given by Chen and Underwood seem to differ a bit from each other, but instead of bothering you with a formula, I’ll simply describe how it works in the example (my understanding being closer to Chen’s formula, I think). Let’s start with the first token of the first document (although the order doesn’t matter), ‘like’, currently assigned to topic A.
Should ‘like’ belong to topic B instead? If ‘like’ belonged to topic B, 3 out of 4 tokens in this document would belong to the same topic, as opposed to 2:2 if we stay with topic A. On the other hand, changing ‘like’ to topic B would threaten the equilibrium of topics over all documents: topic B would consist of 12 tokens and topic A of only 10, as opposed to the perfect 11:11 equilibrium if ‘like’ remains in topic A. In this case, the former consideration outweighs the latter, as the two factors get multiplied: the probability for ‘change this token to topic B’ is 3/4 * 1/12 = 6%, whereas the probability for ‘stay with topic A’ is 2/4 * 1/11 = 4.5%. We can also convert these numbers to absolute percentages (so that they add up to 100%) and say: ‘like’ is 57% topic B and 43% topic A.
What are you supposed to do with these percentages? We’ll get there in a minute. Let’s first calculate them for the next token, ‘eat’, because it’s one of those interesting lemmata with multiple tokens in our corpus. Currently, ‘eat’ in the first document is assigned to topic B, but in the second document it’s assigned to topic A. The probability for ‘eat stays in topic B’ is the same as the same as for ‘like stays in topic A’ above: within this document, the ratio of ‘B’ tokens to ‘A’ tokens is 2:2, which gives us 2/4 or 0.5 for the first factor; ‘eat’ would be 1 out of 11 tokens that make up topic B across all documents, giving us 1/11 for the second factor. The probability for ‘change eat to topic A’ is much higher, though, because there is already another ‘eat’ token assigned to this topic in another document. The first factor is 3/4 again, but the second is 2/12, because out of the 12 tokens that would make up topic A if we changed this token to topic A, 2 tokens would be of the same lemma, ‘eat’. In percentages, this means: this first ‘eat’ token is 74% topic A and only 26% topic B.
In this way we can calculate probabilities for each token in the corpus. Then we randomly assign new topics to each token, only this time not on a 50:50 basis, but according to the percentages we’ve figured out before. So this time, it’s more likely that ‘like’ will end up in topic B, but there’s still a 43% chance it will get assigned to topic A again. The new distribution of topics might be slightly better than the first one, but depending on how lucky you were with the random assignment in the beginning, it’s still unlikely that all tokens pertaining to food are neatly put in one topic and the animal tokens in the other.
The solution is to iterate: repeat the process of probability calculations with the new topic assignments, then randomly assign new topics based on the latest probabilities, and so on. After a couple of thousand iterations, the probabilities should make more sense. Ideally, there should now be some tokens with high percentages for each topic, so that both topics are clearly defined.
Only with this example, it doesn’t work out. After 10,000 iterations, the LDA script I’ve written produces results like this:
- topic A: cute (88%), like (79%), chinchilla (77%), hamster (76%), …
- topic B: kitten (89%), sister (79%), adopt (79%), yesterday (79%), …
As you can see, words from the ‘animals’ category ended up in both topics, so this result is worthless. The result given by Mallet after 10,000 iterations is slightly better:
- topic 0: cute kitten broccoli munch hamster look yesterday sister chinchilla spinach
- topic 1: banana eat piece adopt breakfast smoothie like
Topic 0 is clearly the ‘animal’ topic here. Words like ‘broccoli’ and ‘much’ slipped in because they occur in the mixed-topic sentence, “Look at this cute hamster munching on a piece of broccoli”. No idea why ‘spinach’ is in there too though. It’s equally puzzling that ‘adopt’ somehow crept into topic 1, which otherwise can be identified as the ‘food’ topic.
The reason for this ostensible failure of the LDA algorithm is probably the small size of the test data set. The results become more convincing the greater the number of tokens per document.
For a real-world example with more tokens, I have selected some X-Men comics. The idea is that because they are about similar subject matters, we can expect some words to be used in multiple texts from which topics can be inferred. This new test corpus consists of the first 100 tokens (after stop word removal) from each of the following comic books that I more or less randomly pulled from my longbox/shelf: Astonishing X-Men #1 (1995) by Scott Lobdell, Ultimate X-Men #1 (2001) by Mark Millar, and Civil War: X-Men #1 (2006) by David Hine. All three comics open with captions or dialogue with relatively general remarks about the ‘mutant question’ (i.e. government action / legislation against mutants, human rights of mutants) and human-mutant relations, so that otherwise uncommon lemmata such as ‘mutant’, ‘human’ or ‘sentinel’ occur in all three of them. To increase the number of documents, I have split each 100-token batch into two parts at semantically meaningful points, e.g. when the text changes from captions to dialogue in AXM, or after the voice from the television is finished in CW:XM.
I then ran my LDA script (as described above) over these 6 documents with ~300 tokens, again with the assumption that there are 2 equally distributed topics (because I had carelessly hard-coded this number of topics in the script and now I’m too lazy to re-write it). This is the result after 1,000 iterations:
- topic A: x-men (95%), sentinel (93%), sentinel (91%), story (91%), different (90%), …
- topic B: day (89%), kitty (86%), die (86%), …
So topic A looks like the ‘mutant question’ issue with tokens like ‘x-men’ and two times ‘sentinel’, even though ‘mutant’ itself isn’t among the high-scoring tokens. Topic B, on the other hand, makes less sense (Kitty Pryde only appears in CW:XM, so that ‘kitty’ occurs in merely 2 of the 6 documents), and its highest percentages are also much lower than those in topic A. Maybe this means that there’s only one actual topic in this corpus.
Running Mallet over this corpus (2 topics, 10,000 iterations) yields an even less useful result. The first 5 words in each topic are:
- topic 0: mutant, know, x-men, ask, cooper
- topic 1: say, sentinel, morph, try, ready
(Valerie Cooper and Morph are characters that appear in only one comic, CW:XM and AXM, respectively.)
Topic 0 at least associates ‘x-men’ with ‘mutant’, but then again, ‘sentinel’ is assigned to the other topic. Thus neither topic can be related to an intuitively perceived theme in the comics. It’s clear how these topics were generated though: there’s only 1 document in which ‘sentinel’ doesn’t occur, the first half of the CW:XM excerpt, in which Valerie Cooper is interviewed on television. But ‘x-men’ and ‘mutant’ do occur in this document, the latter even twice, and also ‘know’ occurs more frequently (3 times) here than in other documents.
So the results from Mallet and maybe even my own Perl script seem to be correct, in the sense that the LDA algorithm has been properly performed and one can see from the results how the algorithm got there. But what’s the point of having ‘topics’ that can’t be matched to what we intuitively perceive as themes in a text?
The problem with our two example corpora here was, they were still not large enough for LDA to yield meaningful results. As with all statistical methods, LDA works better the larger the corpus. In fact, the idea of such methods is that they are best applied to amounts of text that are too large for a human to read. Therefore, LDA might be not that useful for disciplines (such as comics studies) in which it’s difficult to gather large text corpora in digital form. But do feel free to e.g. randomly download texts from Wikisource, and you’ll find that within them, LDA is able to successfully detect clusters of words that occur in semantically similar documents.
These four issues constitute a story arc of their own (titled “Incarnations”), the end of which is also marked by Greg Smallwood’s return as the sole artist from the next issue on, so it makes sense to review them now.
Authors: Jeff Lemire (writer); Greg Smallwood, Wilfredo Torres, Francesco Francavilla & James Stokoe (artists); Jordie Bellaire & Michael Garland (colourists)
Pages per issue: 20
Price per issue: $3.99
Previously in Moon Knight: Moon Knight has escaped from the mental asylum but then met his patron god Khonshu, fell out with him, jumped from a pyramid, passed out and awoke in his Steven Grant persona. He is producing a film starring his girlfriend Marlene as the female lead. Everything seems fine and the last panel of issue #5 shows a smiling Steven.
And here his troubles begin. Our protagonist keeps involuntarily changing in and out of his identities, and his surroundings change with him. Everywhere he is haunted by incarnations of his tormentors at the mental asylum, nurses Bobby and Billy and psychiatrist Dr Emmet. And also by werewolves from outer space.
Neither Moon Knight nor the readers know which reality is actually the real one. The guest artists reduce the subtlety somewhat, but it is also an interesting gimmick that each of Moon Knight’s personas/realities is drawn by different artists: taxi driver Jake Lockley by Francesco Francavilla, film producer Steven Grant by Wilfredo Torres and Michael Garland, and space pilot Marc Spector by James Stokoe.
So how exactly does this brilliant device of switching back-and-forth between identities work? Jeff Lemire employs a variety of ways to do this, but let’s take a closer look at the beginning of this arc in issue #6. The first panel (art by Torres and Garland) shows Moon Knight in his old cape fighting some villain in what looks like ancient Egypt. So far, this could be a classic Moon Knight story. In the second panel though, a speech bubble is partly obscured by a boom microphone, and on the following double page we learn that this was only a Moon Knight film being shot, produced by Steven Grant. The name of the leading actor though, whose face we never get to see, is Marc Spector – the real name of the real Moon Knight!
On page 5, Steven and Marlene enter a taxi and talk about a fundraising event at a mental hospital (because their film “explores some real themes… identity, mental illness”), which of course later turns out to be the hospital where Moon Knight was detained earlier. The last panel on this page contains a caption: “Steven Grant is too soft for what comes next…”, and on the next page (from now on drawn by Francavilla) their taxi driver turns out to be Jake Lockley! After he has dropped Steven and Marlene off, he meets his friend Crawley, who remembers the events from the first arc (the escape from the mental hospital) but Jake can’t.
Crawley tells Jake on p. 9, “You’re in the hospital right now”, then disappears. Jake opens the trunk of his taxi where he keeps his Moon Knight costume. The following page is drawn by Torres and Garland again, and on the first panel we see Steven Grant looking at his dinner suit (which looks not unlike Moon Knight’s ‘Mr Knight’ costume) on his bed from the same perspective. Apparently he and Marlene are getting ready for their fundraising party at the hospital. Steven is confused and says to Marlene, “I – I was somewhere else! I was in this cab and there was this man, this old man with white hair, and he told me – he told me I was in a mental hospital.”
Marlene answers, “Have you been taking your meds? […] You remember last time you got off, how you got.” So (according to this version of Marlene) Steven is mentally ill, which would explain the Jake Lockley scene as Steven’s delusion. But Steven doesn’t even remember being on medication at all.
And so it goes on. It’s a joy for the reader to gradually realise on how many levels the various realities are intertwined, and how they all contradict each other. Until issue #9 when Moon Knight, in his Mr Knight outfit and drawn by Greg Smallwood and Jordie Bellaire again, confronts his other three personas, defeats or makes peace with them, and they vanish.
The “Incarnations” story arc was one wild ride, and if Lemire, Smallwood and Bellaire keep up their good work in the next arc, Moon Knight will surely be the best current Marvel comic, now that The Vision has ended.
Rating: ● ● ● ● ○
Arjun Appadurai’s book Modernity at Large. Cultural Dimensions of Globalization was published in 1996 but is based on texts written around 1990. Its core is the chapter, “Disjuncture and Difference in the Global Cultural Economy” (27-47), first published as a journal article in 1990. Thus it can still be seen as a continuation of the discourse on postmodernism/postmodernity from the 1980s (as reflected on this weblog by the series of posts on texts from 1980 to 1985).
The new element that Appadurai brings to the postmodernist discussion is globalisation: his aim is “to construct what John Hinkson calls a ‘social theory of postmodernity’ that is adequately global” (47), although Appadurai usually speaks more often of “modern” when he means the present day. The important point, though, is the rupture or paradigm shift that he suggests to have occurred around 1970: “it is only in the past two decades or so that media and migration have become so massively globalized, that is to say, active across large and irregular transnational terrains” (9).
This leads to the present-day “new global cultural economy” (32) that needs to be analysed by a framework of five “dimensions of global cultural flows” (33):
- ethnoscapes, i.e. the flow of people,
- mediascapes, i.e. mass media and the images and information they convey,
- technoscapes, i.e. the distribution of high-tech knowledge, machinery, and skills,
- financescapes, i.e. “the disposition of global capital” (34), and
- ideoscapes, i.e. “meaning-streams” in “the discourse of democracy” (37) and other ideologies and concepts.
It would be easy to apply this framework to comics as commodities, i.e. comic books, TPBs, tankobon etc., the production and reception of which are nowadays almost always transnational processes. But are these global cultural flows also reflected in the content of comic stories? While this is not meant by Appadurai as a characteristic of postmodern cultural works, it is not far-fetched to expect that postmodern works are more likely to reflect a global cultural economy than previous ones.
This also gives me the opportunity to write about a comic that more should be written about (though it surely will be included in many end-of-year lists for 2016) because of its outstanding quality: The Vision (I keep seeing the title given simply as Vision, but on the covers it clearly says The Vision) by writer Tom King, artist Gabriel Hernández Walta and colourist Jordie Bellaire. Across the 12 issues, I found the following traces of Appadurai’s landscapes:
- ethnoscape: the series is about the ‘synthezoid’ Vision having created an artificial family – wife, daughter and son – and moving into a house in Arlington, Virginia. This, and their difficulties of settling in among humans, are of course metaphors for transnational migration and xenophobia. But there is also proper migration represented or at least implied in The Vision: in #4, the children, Vin and Viv, play with a football that has “Fighting Redskins” and a caricature of a Native American printed on it. It’s the mascot of their high school, they explain to Vision, and only recently has it been changed to the “Fighting Patriot”, a politically correct “colorful bull in a three-corner hat”. This little episode brings to mind that naturally, there are only few Americans whose ancestors were not transnational migrants.
Then there are characters in this comic who represent, through their name and/or appearance, more recent immigration waves than the Mayflower – Leon Kinzky, the Asian-looking Matt Lin, and Marianella Mancha. Her son Victor Mancha even draws a connection between himself and the Spaniard Don Quijote de la Mancha on the sole basis of their names (in #8).
Finally, there is a long quote from Shakespeare’s The Merchant of Venice about being Jewish.
- mediascape: specifically, Appadurai means electronic media such as television (3, 35), so the play The Merchant of Venice first shown as a hardcopy book in #5, though written in England, doesn’t count. Although there is some talk of “downloading” and “uploading” things and some smartphones are shown, there are few instances of content being electronically mediated across national boundaries. One example is Vin “downloading Bach’s cello concerto” in #3 – while we are not told where the recording was made, at least the composer is German.
- technoscape: a series with androids as protagonists is bound to feature lots of high-tech machinery, but the sources of all these gadgets are Ultron, Vision and Tony Stark – so I think it’s all ‘made in USA’. No transnational flow here.
- financescape: in the beginning of the comic, Vision mentions his difficulties in getting a steady income, and Tony Stark, the embodiment of wealth in the Marvel universe, appears a few times. Apart from that, financial matters don’t play any role in The Vision, let alone transnational financial flows.
- ideoscape: The Vision is quite a cerebral comic, but few ideas that can be traced back to outside the US are mentioned. In #9, however, Victor Mancha says: “Vin’s reading this book [The Merchant of Venice] over and over. Like he’s obsessed with mercy and justice.” So some ideas have travelled from England to America after all.
To sum up, applying Appadurai’s framework to the content of a (supposedly postmodern) comic doesn’t yield as many representations of global cultural flows as I had expected. But, again, that’s not what it was intended for. Applying this framework to the para- and extratextual information pertaining to a comic, however, would surely reveal it as a product of Appadurai’s global cultural economy.
Authors: Chelsea Cain (writer), Kate Niemczyk (artist), Rachelle Rosenberg (colourist)
Publication Date: October 2016
Price: $ 3.99
I don’t usually review single comic book issues, but Mockingbird #8 merits an exception for two reasons:
- This eighth issue is already the last one of this series, and when it came out, writer Chelsea Cain tweeted: “Please buy Mockingbird #8 this Wed. Send a message to @marvel that there’s room in comics for super hero stories about grown-up women.”
Furthermore, there was some scandal about Cain being harassed on twitter, leading to more pleas for solidarity with Cain. So far, this campaign hasn’t had any effect on the sales of Mockingbird #8, but sales figures are based on retailers’ purchase decisions made months ago, so who knows, maybe this solidarity campaign will make an impact after all. Plus, many people seem to buy the first trade paperback instead.
- And then there’s the cover. Comic book covers are always made to be eye-catchers, but this one stands out as one of the most iconic covers of at least this year. In contrast to many other covers, it even reflects the contents of the comic, as Mockingbird is shown wearing this t-shirt on 5 panels on the penultimate page.
“ASK ME ABOUT MY FEMINIST AGENDA”? That’s just what we’re going to do now: does Bobbi Morse a.k.a. Mockingbird have a feminist agenda? The short answer is, there would be no reason to wear that t-shirt if she didn’t. For the long answer, there are four key scenes with regard to feminism that merit a closer look:
- p. 5: “I’m the law on this boat, Slade”, Mockingbird says to the Phantom Rider when he comes to haunt her on a boat cruise. Superheroes often take the law into their own hands and act as ad hoc commanders of civilian groups (as in this case, the cruise passengers). Here, a woman assumes leadership over a group of both women and men. The fundamental possibility to do so is a classic feminist claim. On the other hand, this gender perspective is not made explicit.
- p. 16: “He divorced me because I cheated on him. He told himself that you had drugged me, taken advantage of me, but he never truly believed it. It’s too ridiculous. He knows that I’ve always made my own decisions. And that I’ll live with the consequences.” Divorce is a traditional feminist device for sexual self-determination, but here it’s Bobbi’s husband who divorced her, not the other way round. However, there is also a discussion (at least from what I gather online – not sure about serious feminist theory) whether cheating can be considered a feminist practice to achieve sexual self-determination. In this case, though, it looks as if Mockingbird regrets her extramarital affair. (For more information on this piece of backstory, see e.g. this review on xmenxpert.)
- p. 19: “This doesn’t count as a rescue”, Bobbi says to Hunter when he comes in a helicopter to rescue her from some beach to which she was swept after she had gone overboard the cruise ship. What could have easily turned into a ‘damsel in distress’ scene is put into perspective by Bobbi’s lines of dialogue and the beach resort setting: she clearly didn’t suffer hardship alone on that beach. Then again, the action in this scene remains the same: when the man comes to take her away from the lonely island / family resort, she lets him. Or is this just an instance of the controversial opinion that feminism and male ‘chivalry’ are reconcilable?
- p. 20: “We’re here because I need a foot rub.” The comic ends with a scene that Chelsea Cain describes in the epilogue as an “alpine threesome”. A male-male-female threesome in which the woman is clearly dominant? That surely is a feminist sexual practice if there ever was one.
In that same epilogue text, Cain describes Bobbi as “separate from the male gaze, but still not afraid to bask in it” – and there is indeed some basking going on in this comic, though not as much as in others. All things considered, while Bobbi may not have an explicit, discernible feminist agenda in Mockingbird #8, there are much more subtle and not-so-subtle feminist undertones in this comic than in most other mainstream superhero comics.
That alone makes Mockingbird #8 an outstanding comic book, but it’s also beautifully drawn (and coloured), has some genuinely funny moments, and many fresh and wacky ideas. Ultimately Mockingbird proved too over-the-top for either the readers or the editorial management of Marvel, but I hope this won’t be the last we get to see of Cain and Niemczyk.
One of the many series recently rebooted by Marvel was Moon Knight, and as Moon Knight is a character I tend to follow (see my previous reviews of his series: Moon Knight (2011) #6-8 and #9-12, Moon Knight (2014) #1-3 and #4-6), I thought I’d give him another try.
Authors: Jeff Lemire (writer), Greg Smallwood (artist), Jordie Bellaire (colourist)
Pages per issue: 20
Price per issue: $3.99
Previously in Moon Knight: No idea what happened at the end of the previous series, because I dropped it when Warren Ellis left after only six issues. (Maybe I should have stuck to it, because it turns out Greg Smallwood’s artwork is almost as striking as Declan Shalvey’s who left the book shortly after Ellis…)
One of Moon Knight’s/Marc Spector’s defining characteristics is his precarious mental health, so it makes sense for Jeff Lemire to start the story with Marc being a patient – or should we say ‘inmate’? – in a mental hospital. Marc has these memories about being Moon Knight, but none about how he got there, and the hospital psychiatrist tells him that he has been there since he was twelve years old. Then again, he has these visions of his Egyptian patron deity, Khonshu, which suggest to him that the hospital staff are in fact other, evil Egyptian mythological beings.
Mental asylum break stories (if that’s a thing) are powerful when they manage to convey the feeling of despair in the protagonist: he or she is the only one who knows what’s really going on, but everyone else thinks he or she is just crazy (think Terminator 2: Judgment Day). This Moon Knight series adds the thrill of leaving the reader in the dark, at least initially, about which is the truth and which is Marc’s imagination: we are shown both the hospital staff and, alternately, the Egyptian gods, but only one of the two can be real (think David Cronenberg’s Spider).
One of the few things in which Ellis didn’t succeed in his run was the handling of Moon Knight’s backstory. Lemire achieves this by including several of Moon Knight’s supporting cast, and by putting more emphasis on his different personas (the millionaire, the taxi driver).
And then there’s the art. Often there are only few panels on a page, of different size and horizontally centered so that there is a lot of white space, giving a massive, iconic, grave and simply powerful impression. Some guest artists were involved in issue #5, which makes sense because each draws a different scene in a dream (?) sequence. Alas, from the solicitations it looks like Smallwood leaves the series after #6.
If there’s one thing I don’t like about this Moon Knight, it’s that the Egyptian gods often don’t talk like one would expect gods to talk, thus appearing less awe-inspiring than they could be. Granted, deities in the Marvel Universe are more mundane beings than the omnipotent gods from ‘real’ mythological tradition, but still…
All things considered, this might indeed be the best Moon Knight series ever, and (together with The Vision) the best Marvel book right now.
Depending on where you live, May 1st may have some connection, historically or actually, to labour and workers’ rights, or even socialism and class struggle. On this occasion I thought I’d write a little blogpost about politics and comics. Some years ago at a conference, I attended a talk on a certain political or ideological stance in the comics of Warren Ellis,¹ which made me wonder what stance, if any, can be found in one of his latest comics, his short-lived Moon Knight run from 2014.
Each of the 6 issues is beautifully illustrated by Declan Shalvey and Jordie Bellaire, and tells a largely self-contained story. I have already reviewed issues #1-3 on this weblog, so today we’re going to look at the second half of the run, which consists of the stories “Sleep”, “Scarlet”, and “Spectre”.
Politics is sometimes defined as “beliefs and attitudes about how government should work” (Macmillan), and that is the definition with which we’ll work here. At first glance, there seems to be nothing political about these typical masked vigilante stories: Moon Knight comes to the scene of the crime, confronts and eventually defeats the criminal(s). (At least in “Sleep” and “Scarlet”, whereas “Spectre” is told from the antagonist’s point of view, but the result is the same.) On closer inspection, though, society and government appear in all three of the stories.
Government is always present in the struggle between police (i.e. enforcement of the law made by the government) and crime (i.e. defiance of the government and its law). The New York Police Department is portrayed in an unfavourable light: unable to solve the crimes themselves, they rely on Moon Knight, who works outside of the law. Unlike in other superhero stories where police officers try to solve the crime themselves, overextend themselves, get into trouble and need to be rescued by the superheroes, the NYPD in Moon Knight doesn’t even try. Moon Knight does their work for them, and he does it in a way police couldn’t (or shouldn’t), killing, maiming and unnecessarily hurting his opponents instead of arresting them.
The criminals, on the other hand, – including Ryan Trent in “Sleeper” who starts out as one of the ‘good guys’ but ends up killing innocents – are basically given free rein in this New York City. In all of the three stories, their crimes are ultimately avenged by Moon Knight, but only after they were able to placidly commit them. Moon Knight is not one for preventing crime.
Warren Ellis has created a world in which government has failed. To maintain order, it takes a force – Moon Knight – that has the necessary financial and physical power, without being controlled by the government. This is a political vision that has little to do with democracy, in the sense that the people had any control over Moon Knight’s ‘work’. But it has a lot to do with ‘might makes right’ and the ‘longing for the strong man’ – ideas more closely associated with dictatorship. Granted, many superhero comics operate within a similar mindset, but in Moon Knight these ideas are particularly noticeable.
Remember the conference paper I announced on this weblog in 2012? It took some time, but now this paper has been published as an article in Studies in Visual Arts and Communication – an international journal and is available online for free: http://journalonarts.org/wp-content/uploads/2015/01/SVACij-Vol1_No2_2014-delaIGLESIA_Martin-Presence_in_comics.pdf
Here’s the abstract:
The term ‘presence’ is often used to denote a trait of an artwork that causes the feeling in a viewer that a depicted figure is a living being that is really there, although the viewer is aware that this is not actually the case. So far, scholars who have used this term have not explicitly provided criteria for the assessment of the degree of presence in a work of art. However, such criteria are implicitly contained in a number of theoretical texts. Three important criteria for presence appear to be:
1. size – the larger a figure is depicted, the more likely this artwork will instil a feeling of presence.
2. deixis – the more the work is deictically orientated towards the beholder, e.g. if figures seem to look or point at the beholder, the higher the degree of presence.
3. obtrusiveness of medium – if there is a clash of different diegetic levels within an artwork, the degree of presence is reduced.
These criteria can be readily applied to a single image like a painting or a photograph. A comic, however, consists of multiple images, and the presence of each panel is influenced by the panels that surround it by means of contrast and progression. Another typical feature of comics is written text: speech bubbles, captions etc. do not co-exist with the drawings on the same diegetic level, thus betraying the mediality of their panels and reducing their degree of presence. A comic that makes striking use of effects of presence, which makes it a suitable example here, is the superhero series The Ultimates by Mark Millar and Bryan Hitch (Marvel 2002 – 2004). The characters in this comic are often placed on splash pages and/or seemingly address the reader, resulting in a considerable experience of presence.