To my sister Maguy To my fiancée Petty Acknowledgements
Bibliographic record
Abstract
I would like to thank the many individuals who have made this thesis possible. I am especially grateful to my supervisor, Dr. Robert L. Probert, who helped choose the thesis topic and provided guidance, valuable support and encouragement during this research. My thanks also go to the faculty, staff, and friends of Notre Dame University, especially to Fr. Boutros Tarabay and Mr. Abdallah Abi Aad, for their support and encouragement while pursuing my graduate education. I would also like to thank Dr. Stan Matwin from the University of Ottawa and Dr. Tony White from the University of Carleton for their valuable suggestions and comments. I would also like to thank Louise Desrochers and Marie-Jo Worral for their unconditional help. My thanks also go to IBM Canada ltd., especially Joe Wigglesworth, Terry Lau, Paul Sims, Maurus Cappa, Behrad Ghazizadeh, and Philip Day, for their support, comments, suggestions, and encouragement throughout this research. I would also like to thank Spiro Bittar, Tarek Rahal, Claude Jabre, and Rabih Keserwani for their unconditional support and help while implementing and testing case studies conducted throughout this research. My thanks also go to Wael Hassan and Nour El-Kadri for their unconditional help and valuable suggestions. My deepest thanks go to my family (my father Doumit, my mother Mary, my brothers Mark and Vany, and my sister Maguy), whose support and encouragement have helped me pursue my education. Last but not least, my deepest love goes to my fiancée Petty for her love, support, sacrifice, and understanding during the whole period that went into pursuing my graduate studies, conducting this research, and writing this thesis. Table of contents
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.085 | 0.068 |
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".