Bibliographic record
Abstract
In the 1950s, Anne Innis Dagg was a young zoologist with a lifelong love of giraffe and a dream to study them in Africa. Based on extensive journals and letters home, Pursuing Giraffe vividly chronicles the realization of that dream and the year that she spent studying and documenting giraffe behaviour. Dagg was one of the first zoologists to study wild animals in Africa (before Jane Goodall and Dian Fossey); her memoir captures her youthful enthusiasm for her journey, as well as her näiveté about the complex social and political issues in Africa. Once in the field, she recorded the complexities of giraffe social relationships but also learned about human relationships in the context of apartheid in South Africa and colonialism in Tanganyika (Tanzania) and Kenya. Hospitality and friendship were readily extended to her as a white woman, but she was shocked by the racism of the colonial whites in Africa. Reflecting the twenty-three-year-old author’s response to an “exotic” world far removed from the Toronto where she grew up, the book records her visits to Zanzibar and Victoria Falls and her climb of Mount Kilimanjaro. Pursuing Giraffe is a fascinating account that has much to say about the status of women in the mid-twentieth century. The book’s foreword by South African novelist Mark Behr (author of The Smell of Apples and Embrace ) provides further context for and insights into Dagg’s narrative.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.011 |
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".