“My face! give it back!” : interrogating mask metaphors and identification in Scott McCloud's Understanding Comics
Bibliographic record
Abstract
This thesis argues that one way to resolve some of the discrepancies in the theory of identification proposed by Scott McCloud in Understanding Comics, such as his mask metaphor, is to approach his theory via theatrical conceits. By thinking of identification in the terms of an actor playing a masked character, in which to read a comic and identify with a cartoon character means to put on a mask and imaginatively play the character, McCloud’s contention of cartoons matching our basic mind-pictures becomes readily resolved by virtue of the fact that the mask is serving as a dramatic signifier of the reader’s inner reality. That is, by imaginatively bringing to life the iconic cartoon form, the reader mimetically becomes the character, hence making it entirely plausible for anyone to enter the world of the cartoon and see themselves in the faces of the characters. The mask thus becomes a logo that transforms the reader’s body into logos, granting access to the realm of the symbolic by covering up a reader’s personal identity such that he or she becomes a cipher, at liberty to see whatever he or she wants in the cartoon image. However, regarding the comics panel as a kind of dramatic stage in which the identifying reader is intimately involved as both actor and initiator of theatrical communication, raises other problems. It not only problematises the distinction between reality and artifice in an imaginative performance context, but also ignores the fact that masks are frequently used for purposes of preventing rather than promoting audience identification. McCloud’s theory, in attempting to circumvent the issues surrounding the fraught relationship between self and other that are inherent in any discussion of identification by applying the mask as a structuring term, raises new issues of its own.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".