Playing 'Shame': One Technique for Introducing Text Analysis to the Literary Studies Classroom
Bibliographic record
Abstract
A former professor of mine, now gone to his just reward – a character who one might never imagine to find in a David Lodge novel, and yet he was noted in one as a poor soul banished in the late 1960s from glitzy, big-shoulder US academic culture to the pastoral Canadian prairies we all know and love – gave me some of the most useful pragmatic advice I’d ever received from an academic up to the point that I'd received it. He suggested that all of us concern ourselves as much with the expanding of our own knowledge as we do with concealing those areas in which we have little expertise or experience. This was heady stuff for me (I was quite a few years younger, then), but it was an apt observation. And when I think of the focus of this panel – ‘playing with text analysis’ – his words resonate....
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".