'If people want to turn away from the subjec t matter, you have to give them a reason to turn back to it.' Interview with Jo-Anne McArthur, leading animal photojournalist
Bibliographic record
Abstract
Jo-Anne McArthur (Ottawa, 1976) is an award-winning photographer, author, editor and public speaker who has dedicated her career to animal activism. She is the founder and president of the first Animal Photojournalism (APJ) agency, We Animals Media, which visually documents the global nature of animal suffering in multiple exploitation industries, including factory farming and animal agriculture. McArthur is the author of We Animals (2014), Captive (2017) and HIDDEN: Animals in the Anthropocene (2020), and her work has also been appeared in National Geographic, National Geographic Traveller, The Washington Post and The Guardian, among other publications and media. Her commitment and dedication to her craft were portrayed in Liz Marshall's highly acclaimed documentary, The Ghosts in Our Machine.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".