SHAPEREADER: A Forward Answer To a Lack of Inclusivity Regarding Visual Disabilities in Comics (and Visual Narratives).
Bibliographic record
Abstract
Daniel Jiménez Quiroz (MAAAP '20) is organizing with the support of the Wellness Center at SAIC: SHAPEREADER: A Forward Answer To a Lack of Inclusivity Regarding Visual Disabilities in Comics (and Visual Narratives). The workshop will happen this Tuesday, April 2nd in Sharp 327 between 10 and 12. RSVP to djimen1@saic.edu to save a spot for this event. This is a unique occasion for visually impaired and sighted participants to meet and talk side by side about tactile narratives based on an ad-hoc language. The goal of the workshop is not merely to reflect the community’s expression by a collectively-built story, but to provide the very same genetic material for textual production: a repertoire of empty symbols that can be reiteratively attributed different meanings and functions according to each community’s specific needs, preoccupations and issues. The activity will be conducted by Ilan Manouach (Athens, 1980), a multidisciplinary artist with a specific interest in conceptual comics. https://ilanmanouach.com/ He is currently a Ph.D. researcher at the Aalto University in Helsinki. Manouach is recognized for his contributions to the dialogue of the "reader’s space” and the fluid archives, "beyond all imposed meanings coming from the author or the prevailing readings of certain works." He is a usual contributor to Monoskop and UbuWeb. https://www.facebook.com/100057442870227/posts/2051939518237303/
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".