Bibliographic record
Abstract
It was at one of the U.K. Association of Teachers of Mathematics meetings, probably in the early or mid-1970s, that I first met David Wheeler. I am not sure how it came about that he always supported my interest in mathematics and the visual arts, but it was certainly he who gave me my first opportunity to give a course in this area when he invited me to teach a summer session for teachers at Concordia University in Montreal. He gave me free reign and much encouragement, but occasionally this came with a sprinkling of constructive suggestions and a twinkle in his eye. Unfortunately, lam not able to locate the correspondence dealing with the session on *mathematizing ' that he, Eric Love, John Trivett and I carried on as part of the preparation for our session at ICME IV in San Francisco in 1980. I do recall though that I was not successful in convincing him that I really did not fully understand what was meant by that term. But he assured me that I did it all the time, and it is true I did manage to give a talk on 'mathematizing with apiece of paper'. [1] From the launch ofFLM in 1980 until issue 50 when he retired as editor, David was concerned with the visual aspects of the journal. A few quotations from some of his letters to me will indicate this:
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.126 | 0.042 |
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".