Bibliographic record
Abstract
It is a truth universally acknowledged… that defining "prejudice" is a highly contextual and laborious task. 1 As I grappled with this challenge while writing the preface for the GLSA Research Series, I couldn't help but wonder what Jane Austen would think about us using her work, Pride and Prejudice, as an inspiration for the 16 th Annual McGill Graduate Law Conference and this journal.Her wit and humour, so evident in her novels and letters, lead me to believe that she would have been delighted to see her beloved novel become the subject of an academic conference for law graduates.But what was her understanding of the term "prejudice" when she chose it for the title of her book?Jane's first draft of Pride and Prejudice was titled First Impressions and was rejected by publishers in 1797.Although it is believed that the text of the initial draft was edited before the novel was published in 1813, one can still see a connection between the two titles.Did Jane, then, believe there was a correlation between prejudice and first impressions?Set in 19 th Century rural England, Pride and Prejudice follows the story of the Bennet family and centers around the burgeoning relationship between Elizabeth Bennet, the second daughter of a modest country gentleman and Fitzwilliam Darcy, a wealthy aristocrat. 2 While some argue that Pride
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.034 | 0.014 |
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".