The killing of Cecil the Lion as an impetus for policy change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The killing of Cecil the Lion in July 2015 generated considerable media attention worldwide. We measured public interest in Cecil's death to examine the degree to which this high-profile incident represented the type of focusing event that public policy scholars often emphasize as being important for triggering policy change. Finding that public interest in lion conservation spiked in the weeks immediately following the incident, we then analysed whether this focusing event led to policy changes to restrict trophy hunting in eight countries (USA, Spain, France, Russia, Canada, South Africa, Germany and Mexico) that most frequently import lion Panthera leo trophies. The surge in public attention seems to have had only a limited impact on the adoption of significant new policy, although it may have hastened changes in some countries.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it