'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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.013 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.010 | 0.025 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".