The Question Concerning Comics as Technology [Slides]
Bibliographic record
Abstract
A presentation by Dr Peter Wilkins (Douglas College, Canada) and Dr Ernesto Priego (City University London, UK). Presented on Thursday 16 June 2016 at the Poetics of the Algorithm. Narrative, the Digital, and ‘Unidentified’ Media conference, University of Liège, Belgium, 15-19 June 2016. This resource has been shared online immediately after presentation and some minor typos might remain. The actual presentation version might have differed slightly from the content on this file. Poetics of the Algorithm: Narrative, the Digital, and ‘Unidentified’ Media was an international and bilingual conference organized by the ACME Comics Research Group and hosted by the University of Liège (Belgium), from June 16 to June 18, 2016. The conference focused on interactive fiction, apps, digital comics, games, e-literature and other emerging, ‘new’ media. The conference will host workshops, roundtable discussions, panels, and presentations of papers. With the exception of third-party content, this deck of slides and its presenters’ notes are licensed under a Creative Commons Attribution 4.0 International License. This document is a scholarly output. If you use it in any way please good academic practice requires you cite it and attribute it as you would do with any other scholarly resource. Kindly use the citation information above. Thank you.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.065 | 0.020 |
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".