Bibliographic record
Abstract
Years ago as a Grade 13 student in a small southern Ontario town, I had no firm idea what I wanted to do with my life. On one point, however, I was certain: I had not the slightest interest in joining the family clothing business on Main Street. To his credit, and my profound gratitude, not once did my father pressure me on this, and he did what he could to see I got to university. I had briefly toyed with the idea of studying for the ministry. One of my uncles at the time was a successful minister in a large Presbyterian church in West Toronto and I was somewhat enamoured of the "calling" as a result. But there was more romance than reality to my thinking here. Mathematics and science had been of greater interest to me in high school than history and literature-the usual fare for anyone aspiring to wear the cloth. I had been particularly drawn to trigonometry and Euclidean geometry and the ways of thinking they involved, even to the point of taking satisfaction in the little ritual of entering "QED" at the end of my deductive proofs. In the end, I elected to study engineering and was admitted to the Faculty of Applied Science and Engineering at the University of Toronto.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 0.000 |
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 teacher head, 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".