Bibliographic record
Abstract
As I was growing up in Toronto, my life's ambition was to go to Africa and study the giraffe there.In 1956, when I was twenty-three years old, my dream came true.This book is based on an extensive journal I kept during this adventure, the high point of my life, and on long, excited, twice-weekly letters I wrote home to Canada.It focuses first, of course, on giraffe, which I observed mainly on a twenty-thousand-acre ranch in South Africa near Kruger National Park, but also in Tanganyika, Kenya, and Southern Rhodesia.As a result, in part, of this work, I was able later to publish scientific papers not only on the behaviour of giraffe but also on their subspeciation, distribution, food preferences, gaits, and sexual differences in their skulls (Appendix 1).This memoir also has two lesser areas of focus: the culture of racism and colonialism extant in the countries I visited at that time, and my own naïveté as a young woman educated at a private girls' school in Toronto.The racism of most white people in the Africa of that day astonished me; I was unaware of such discrimination because I grew up in Toronto in the 1930s and 1940s, where there were few blacks or recent non-European immigrants, while aboriginal peoples lived largely out of sight on native reserves or in the North.Virtually everyone I knew was white.It did not occur to me that colonization in Africa was mirrored in the treatment of Indians and Inuit (then thought of as Eskimos) in Canada who, at that time, did not even have the vote.Nor did I realize that most Canadians were white because the country had a restrictive immigration policy.Canadians (unjustifiably) felt superior to Americans who, in the 1950s, were in the throes of trying to solve the problems of deeply entrenched racial discrimination.xi
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.584 | 0.374 |
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".