Bibliographic record
Abstract
In the past few months, widely televised tragedies in France, Germany, and Switzerland have spurred politicians to introduce changes in their countries' already strict laws to make them even more restrictive. Perhaps you remember the headlines? A depressed student in Germany ran amok and killed several people in his school after he'd been expelled. In both France and Switzerland, angry individuals have stormed into local councils and began shooting legislators indiscriminately. This is not a new story. We've seen this show before. First, there is a horrible event, say a disturbed student shoots people in a school, or a maniac goes on a rampage in a public place. Media coverage is intense for a few weeks. Experts on television wring their hands in concern about the danger of gun violence. Then the government feels it must do something to protect the public, so the police are given sweeping new powers, or new restrictions are introduced on owning firearms. Afterwards, the media rush off on a new story, and the public forgets. Later, there is another tragedy somewhere else, and the process starts all over again. Does this sound familiar? It should. This has been the pattern followed by virtually every law that has been introduced in the twentieth century around the world. In the 1990s, we’ve seen this drama on television from Australia, Great Britain, the United States, not to mention Canada, as well other countries. It's time to pause and ask a few basic questions. If laws work to prevent criminal violence, why do these events keep occurring? And not just in places where the laws are comparatively lax, but in countries where it is all but impossible for an average person to own a handgun. Guns are banned in schools. How could attacks happen in gun free zones such as schools? This paper is adapted from the Sixth Annual Civitas Conference in Vancouver, British Columbia held April 26 though 28, 2002.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".