Bibliographic record
Abstract
animal presence, 105 combatants, 106 disembodiment, 106 economic activities, 105 mascots, 107 passive receptors, 107 transport, 106 victimization and abuse, 106 Animal turn, 104, 108-110 Anthropocentricism, 87, 97, 104, 113 Apolitical ecology, 17-18, 125-126 Arctic National Wildlife Refuge, 68, 70 Big data, 212 British Petroleum (BP), 52 Brundtland Commission, 32 Canadian Federal Budget, 48 Canadian settler colonialism, 79 Carbon footprint, 170 Carbon-producing activities, 32 Cenovus, 56, 57 Certainty principle, 141 Circular Economy model, 172 Climate change, 2, 30, 38, 70, 72, 74 environmental degradation, 88 inequity, 79-80 sport ecology, 201 Climate Nexus, 7-8 Collaborative research process climate change, 212 informational introductions, 211 methods and materials, 210 model parsimony and option menu, 211 strong with partner, 211-212 Colonial-capitalist displacement, 89 Community Project Committee (CPC), 92 Companion species perspectives animal agency, 113 animal-participant orientation activities, 115 anthropocentrism, 113 commonsense concepts, 113-114 environmental destruction and preservation, 115 environmental impact, 114-115 extended self, 112 horse transportation, 115 human-animal engagements, 111 moral individualism and identification, 111 multispecies approach, 114 sensitizing concepts, 114 sociological process, 112 Corporate Social Responsibility, 31, 52-53 Corporate sponsorships, 51, 52 Cultural knowledge, 58, 165
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.801 | 0.846 |
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".