The Unanticipated Legacy of Howard G. Savage: Reflections on Teaching, Learning, and Practising Zooarchaeology in Ontario
Bibliographic record
Abstract
Dr. Howard G. Savage, one of the founders of zooarchaeology in Canada, taught the Faunal Archaeo-Osteology course at the University of Toronto from the 1970s to the 1990s. Hundreds of students completed the course, taking away a solid appreciation of zooarchaeological data generation, representation, and analysis. In this article, we consider why the course had a profoundly positive influence on so many students and examine how Dr. Savage’s legacy lives on in zooarchaeology in Ontario. We then interrogate the appropriateness of transferring lessons of an undergraduate course into professional approaches and find that this transferring has indirectly resulted in an arbitrary and insufficiently large sample size appearing in government guidelines for professional archaeologists. Similarly, practices that were deemed appropriate in a university course context, such as a tendency to not identify fish vertebrae, have carried over into professional standards and practice and have resulted in biased zooarchaeological datasets. We argue that accepted practices within zooarchaeology in the province need to be revised and strengthened.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".