The <i>Blackfish</i> Effect: Corporate and Policy Change in the Face of Shifting Public Opinion on Captive Cetaceans
Bibliographic record
Abstract
In February 2010, a captive killer whale ( Orcinus orca ), or orca, killed his trainer at SeaWorld Florida. A cascade of events followed, including successful federal enforcement action against SeaWorld for employee safety violations. In 2012 and 2015, nonfiction books about SeaWorld's history with orcas were published; however, the 2013 documentary Blackfish has done the most to raise public awareness of captive orca welfare and trainer safety. It spawned a massive social media response, leading to the so-called " Blackfish Effect." SeaWorld's visitor numbers declined, business partners ended their relationships, and stock price plummeted. In 2012, Georgia Aquarium in Atlanta applied for a permit to import 18 wild-caught beluga whales from Russia; the permit was denied in 2013, the first time a public display permit had ever been denied in the history of the US Marine Mammal Protection Act. In 2014 and 2016, the California legislature considered bills phasing out captive orca exhibits in the state; the 2016 bill passed and became law in January 2017. In November 2015, a similar bill was introduced (and reintroduced in March 2017) in the US House of Representatives. In March 2016 SeaWorld announced it would end its orca breeding program company-wide and in January 2018 the Vancouver Aquarium announced it would no longer display cetaceans. Shifts in public perception of captive cetacean display strongly suggest policy makers should reconsider the legislative and regulatory status quo.
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 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".