Bibliographic record
Abstract
Ce document est protégé par la loi sur le droit d'auteur.L'utilisation des services d'Érudit (y compris la reproduction) est assujettie à sa politique d'utilisation que vous pouvez consulter en ligne. Jane Austen's Vehicular Means of Motion, Exchange and TransmissionI do not write for such dull elves as have not a good deal of ingenuity themselves (Austen to her sister Cassandra, Letters 79).Reading was a challenge posed by Austen for her readers, both familial and other.Studies of Austen have clearly and repeatedly shown that greater familiarity with the social and historical context of her works deepens our appreciation of her characterization, her political and social commentary. 2 Furthermore, it is evident that Austen herself expects her readers (at least some of them) to use such knowledge to judge and assess character accordingly.This paper arises from work for my second edition of Northanger Abbey for Broadview Press during which time I became increasingly aware that carriages and modes of transportation were carrying significance that I did not fully understand.Was I, as a twenty-first century reader, becoming one of those 'dull elves'?What kinds of judgements about character and place would a broader knowledge of horse-drawn transportation allow?En route I discovered exactly how carriages work as external indicators of wealth or social standing but also, and more surprisingly, as indicators of the driver or passenger's 1 Jane Austen, Jane Austen's
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".