OF THE REQUIREMENTS FOR THE PHD DEGREE IN RELIGIOUS STUDIES
Bibliographic record
Abstract
ii Acknowledgements This project would not have been possible without the support and encouragement of many people. I would like to thank my doctoral supervisor, Pierluigi Piovanelli, for his guidance, support, and patience throughout this process. His insight into my research has been absolutely invaluable. I would also like to thank my professors at the University of Ottawa who played an integral role in my doctoral formation. In particular, I would like to thank Theodore de Bruyn for his help at the beginning of my research, as well as Jitse Dijkstra, for his immense contribution to my understanding of Coptic. I would also like to thank Greg Bloomquist of Saint Paul University for allowing me to attend his seminar on Socio-Rhetorical Analysis. I owe a debt of gratitude to all of my friends and colleagues who supported me throughout my studies. In particular, I would like to thank my close friend and colleague Rajiv Bhola for taking the time throughout our studies together to act as a sounding board for ideas. I would also like to acknowledge colleagues and mentors who have passed on in recent years. Carl Kazmierski, who I was fortunate to have as a professor throughout the course of my
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.402 | 0.328 |
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".