Bibliographic record
Abstract
Steve Kupferman is a second year student at the Faculty of Information. Before coming to Toronto, he studied English at SUNY Buffalo, where he cultivated an appreciation for stories from the lives of deceased authors. He expects this appreciation to grow and grow in years to come. The illu-strious dead are an ideal natural re-source: they just keep on accumu-lating. I was at my local public library just a few weeks ago when I had the very experience local public libraries exist to facilitate: I found something I wanted to read – a book I didn‘t know existed when I entered the building. It was a new addition to the library‘s graphic novel collection: F. Scott Fitzgerald‘s The Great Gatsby, adapted by artist Nicki Green-berg (2007). The Great Gatsby is one of the most-read novels in the history of American letters, so one might assume that converting it into a graphic novel (which is, after all, essentially a euphemism for ―comic book‖) would be a serious mistake. Everyone who has ever taken a high school English class is a potential critic. I, personally, approached the book with a little bit of trepidation. F. Scott Fitzgerald is one of my favourite authors. Greenberg‘s take on Gatsby was either going to be very interesting or very infuriating. Actually, the book is charming. Green-berg has a unique visual sensibility, which op-erates on the text at the level of interpretation rather than revision. She gives Fitzgerald‘s characters alien bodies that are all somehow consonant with their dispositions. Daisy Bu-chanan has a round puffball of a head that hovers above her shoulders on a long, skinny neck, as though lighter than air. Gatsby him-
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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.127 | 0.043 |
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".