Bibliographic record
Abstract
As a boy, I saw my dad cry on only three occasions. One was his father’s funeral. The other two involved dead orcas. In the 1970s, he worked as curator of Sealand of the Pacific, a small oceanarium near Victoria, British Columbia, and then for the Seattle Marine Aquarium and Sea World. On both sides of the US-Canadian border, across the Salish Sea, he helped capture killer whales for sale and display—or, as he darkly joked, “for fun and profit.” Tell someone today that your father caught orcas for a living and you might as well declare him a slave trader. Killer whales are arguably the most recognized and beloved wild species on the planet. They are certainly the most profitable display animals in history, and with the 2013 release of Blackfish, their fate became an international cause célèbre. Broadcast and distributed by CNN, the film became one of the most influential documentaries of all time. Already years into my research for this book when the movie came out, I found little in it surprising. But Blackfish turned my father, long conflicted about his past, sharply against orca captivity. He wasn’t alone. Almost overnight, viewers, politicians, and activists turned their sights on Sea World—a multibillion-dollar corporation famous for its killer whale shows. In this debate, it seemed there was no room for nuance or history. Millions around the world simply knew in their hearts that orcas had to be saved from captivity. What they didn’t realize was that, decades earlier, captivity may have saved the world’s orcas. Orcinus orca is the apex predator of the ocean, but that ocean has changed rapidly in recent decades. Following World War II, rising populations and new technology drove humans to plunder the sea as never before, and many regarded killer whales as dangerous pests. By the 1950s, whalers, scientists, and fishermen around the world were killing hundreds, perhaps thousands, per year. In a single expedition, celebrated by Time magazine, US soldiers slaughtered more than one hundred off Iceland. But then a curious thing happened.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.557 | 0.415 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".