Bibliographic record
Abstract
An outbreak of apology has swept the globe. Bill Clinton has apologized for slavery, Tony Blair for British policy during the Irish potato famine. The Canadian government has apologized to indigenous communities for breaking up their families and to Japanese Canadians for putting their families in internment camps during World War II. The Vatican has apologized for its failure to condemn the Nazi treatment of Jews, Queen Elizabeth for the British exploitation of the Maoris. The Japanese government has apologized to Korean women who were forced into prostitution during World War II, and some former government officials in South Africa have apologized for their behaviour during the period of apartheid. Though the Australian Prime Minister has refused to apologize for past treatment of Aborigines, many Australians have taken it upon themselves to make an apology. But does it make sense to say ‘Sorry’? Can it be done without hypocrisy? The following paradox suggests that there is something wrong with the exercise of apologizing for what our ancestors did, or something wrong with common assumptions about such apologies.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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; both teacher heads 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".