Welfare, wool, women and where it began
Bibliographic record
Abstract
Travel Report for Animal Science Award, NZSAP.\n\nI grew up on a sheep station in outback Australia, and later a sheep, beef and goat farm in the south east of South Australia. I studied Agricultural Science as an undergraduate at Adelaide University and did my PhD on the effects of stress hormones on wool at the Waite Institute with Professor Phil Hynd. Phil was the organiser of the joint conference in Adelaide in July 2016, and it was a real homecoming for me to present papers on sheep, back where it began. Although at their conference both societies previously had a whole sessions on wool, there were only two science presentations with wool in the title and neither of those were about wool itself! Times change. Phil Hynd also asked me to chair a session on “Major welfare issues facing the animal industries in Australia” and instructed me “… you will have to make it lively …” and I hope I did not disappoint in this respect. We had a vote at the end of each presentation, and by and large the audience, dominated by scientists, considered that each industry had made good progress on welfare issues. Of particular interest to me was the fact that the six young members in the NZSAP competition, and the first, second, third place and people’s choice winners of the ASAP student presentations were all female. Julie Everett Hincks became the first female recipient of a well-deserved Sir Arthur Ward Award, and the NZSAP committee elected at the AGM was largely feminine. This is a strong sea-change from when I gave my presidential address to NZSAP in 2006. I would sincerely like to thank the New Zealand Society for funding my trip.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.091 | 0.033 |
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".