Bibliographic record
Abstract
In December 1893, The Strand Magazine published Arthur Conan Doyle's story “The Final Problem,” and the public was apoplectic. Doyle had done the unthinkable: he had killed Sherlock Holmes. Reportedly, some 20,000 readers of the magazine immediately canceled their subscriptions. Londoners, it is said, took to wearing black crepe armbands and marched in protest down Fleet Street. In America, where the story was published in McClure's, “Keep Holmes Alive” clubs were formed. On a North American speaking tour, Doyle reminisced, “I assure you, if I had killed a real man I could not have received more vindictive letters than those which poured in upon me after that event.”1 Although extreme, the reaction of Holmes’s fans cannot easily be dismissed as a case of Victorian excess. Art can play a significant role in our emotional lives, and while anger directed at Professor Moriarty for killing Holmes raises a familiar philosophical problem, anger directed at the story's creator raises another one entirely. Some works hold a special place in our lives. And, like other things, or people, that are special to us, love can quickly give rise to anger. There is a special sort of anger that tends to be directed at creators and their choices that is normally reserved for betrayals and gross violations of one's rights—what might be called righteous anger. Our interest here is in determining what—if anything—might justify this level of anger directed at artists.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.049 | 0.019 |
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".