Bibliographic record
Abstract
The Internet Arun Mukherjee, Professor I regrettably forego major seismic moments in my scholarly life to celebrate the advent of the Internet, which multiplied not only my own ability to research in areas that our libraries are so deficient in—the non-Western regions of the world —but also enhanced my ability to explain the embedded cultural aspects of the texts from these areas to my students. It is wonderful to bring up the images of Chaitnya Mahaprabhu, the androgynous devotee of Krishna, when teaching Amitav Ghosh’s The Sea of Poppies to explain the metamorphosis of Nobokrishna Panda. It is exciting to hunt down an intertext, Swinburne’s “The Garden of Proserpine”—in a matter of minutes—that turns out to be so crucial to understanding Anita Desai’s Clear Light of Day. Or to find the significance of “padewar,” the meager share of the crop that Dalits were entitled to in Maharashtra, when researching on Dalit literature. [End Page 19] Arun Mukherjee, Professor English York University Copyright © 2015 Association of Canadian College and University Teachers
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.541 | 0.409 |
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".