To my sister Maguy To my fiancée Petty Acknowledgements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.021 |
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 it