Bibliographic record
Abstract
Unflattening (2015) is the first comic published by Harvard University Press. It is the book version of Nick Sousanis’ PhD dissertation from Teachers College, Columbia University; a project that has commanded the attention of the comics scholarship community precisely because it is comics as scholarship. This is a collaborative book review in the form of a dialogue between two authors, with each of the reviewers asking the other questions about the book; it is an effort at “unflattening.” In the responses, the reviewers have (wilfully?) misunderstood each other and deviated from the question as they pursue their lines of thought. Unflattening is provocative, and critical comments in the review are a result of Sousanis making us think and question. The reviewers hope that this project is not just a one-off, and that Sousanis and others continue to explore thinking through the multimodal medium of comics.
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 | 0.027 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.048 |
| Scholarly communication | 0.022 | 0.041 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.011 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 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".