Bibliographic record
Abstract
This book has been long in coming.I would not have been able to complete this book had I not received kind nudges from many people since my graduate studies in the Laurier-Waterloo joint doctoral program on religious diversity in North America.The theme for this book was germinated by a conversation with my doctoral supervisor, Kay Koppedrayer.The program itself necessitated its PhD students to locate their chosen research topic in North America and to address the issue of religion and diversity.Within these broad and yet limited foci, Kay advised me: "Mitra, think of a topic that would keep you interested and engaged for a long time coming."Although I have been engaged with the topic of this book longer than I should have, I am grateful for that insightful advice and all the support Kay provided throughout my graduate studies.I could not ask for a better supervisor.She was kind yet firm with deadlines.She warned me not to get fooled by the self-delusional and procrastinating mental statement: "it is all in the mind."Her advice -"there is no paper or chapter of your dissertation until and unless you put it in written words" -helped me to complete the dissertation in time.I am extremely grateful for her superb mentorship.Kay was the epitome of responsible and effective academic mentoring.Let me also express my gratitude to others who helped me complete my graduate studies, from which this book emerged.Janet McLellan and Darrol Bryant served as members of my PhD committee.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.584 | 0.373 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".