Bibliographic record
Abstract
It all began when, as a toddler, I saw the giraffe at the Brookfield Zoo in Chicago. I was so captivated that I later studied biology at the University of Toronto, hoping to learn everything about the species. This didn’t happen – there was no interest then in Africa or in animal behaviour in academic biology. After graduating, my aim was to go to Africa to study giraffe as soon as possible, but I had no contacts there to make this happen. I decided instead to do graduate work for a Master’s degree at the university while I wrote letters to see who might help me accomplish my dream. This took many months – letters to government officials or wildlife departments in countries where there were giraffe, letters to names of people dredged up by friends, letters to professors connected with Africa, even letters to L. S. B. Leakey who was to launch Jane Goodall on her career five years later. After early rebuffs I used initials for my signature so the recipient would presume I was a man, but this did not help. Luckily, about that time Rufus (C. S.) Churcher came from Africa to earn his doctorate at the University of Toronto; he would go on to become a professor there and author of a definitive work on fossil giraffe, ‘Giraffidae’ (1978). He told me about a professor he had studied with, Jakes Ewer of Rhodes University in Grahamstown, South Africa, who might be able to help me. Jakes and his wife, Griff Ewer, were both willing to do this. They put me in touch with Alexander Matthew who managed a citrus and cattle ranch near the Kruger National Park on which roamed nearly 100 giraffe; after some hesitation – he had assumed I was a man – he finally agreed to have me live and work at his ranch. These amazing people became friends of mine for life.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.420 | 0.251 |
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".