0. REFERENCES ii Acknowledgements
Bibliographic record
Abstract
Many thanks to all the members of my committee for persisting to the close of this long project. I am most grateful to everyone involved, especially to co-supervisor Ann Schabas, who sustained her interest in the research through a number of difficulties. Graeme Hirst, also a co-supervisor, opened many doors to things that would otherwise not have been accessible. Special thanks are due to the Department of Computer Science which has welcomed me warmly and has been generous with computing facilities, tutelage and friendship. Past Chairman, Derek Corneil, has my gratitude for letting me share the limited space. In addition, I must say ‘‘thank you’ ’ to John Mylopoulos who encouraged me to study AI and gave me a good start. The funding I received from the Ontario Government and the University of Toronto made further education late in life possible. Support from The Natural Sciences and Engineering Research Council made the difference between good schooling and a fine education. Thanks go also to Jim Dick for his unfailing support and for sharing his family with me when my own died. The last word is for my father who gave me life and helped me to survive it. He loved me and taught me through the days of his life and the days of his death. I Myr— 0. REFERENCES iii A conceptual, case-relation representation of text for information retrieval
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.
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 | 0.001 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.255 | 0.200 |
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".