Bibliographic record
Abstract
This ambitious research study relied on the support provided by many research assistants across Canada who worked with me along the way.I am indebted to their invaluable assistance in community outreach and accessing participants for this study, interviewing, attending events, note-taking, and transcription, as well as putting together annotated bibliographies and data bases.First and foremost, this study owes a great deal to the tremendous support and invaluable contributions of Asma Bala.She worked closely with me as a research assistant during all phases of this study, and the fieldwork we undertook together benefited greatly from her intellectual observations, competency, and talent.Asma leveraged her strong community networks as part of the outreach for this project.She is well respected in the Muslim community, which was important in developing the trust necessary for participants to feel safe coming forward and sharing their stories.Together we spent countless hours driving to interviews and events and, during those drives, discussing and debriefing what we were learning.We sat with our notebooks and laptops in cafés, restaurants, at my university office, or in my home drinking chai and talking about the themes emerging in our fieldwork.I'm grateful to have had her company, dedication, and informative insights throughout this undertaking.We also co-authored a book chapter entitled "Faith and Activism: Muslim Students' Associations and Campus-Based Social Movements," in Political Muslims,
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.216 | 0.139 |
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".