Bibliographic record
Abstract
This Article looks in detail at a very large positive externality which is overlooked in by firearms prohibitionists: the major role that widespread gun ownership plays in reducing the rate of home invasion burglaries (a.k.a. hot burglaries). Because potential burglars cannot tell which homes possess guns, most burglars choose to avoid entry into any occupied home, for fear of getting shot. The entry pattern of American burglars contrasts sharply with that of burglars in other nations; in Canada and Great Britain, burglars prefer to find the residents at home, since alarms will be turned off, and wallets and purses will be available for the taking.Consequently, American homes which do not have guns enjoy significant free rider benefits. Gun owners bear financial and other burdens of gun ownership; but gun-free and gun-owning homes enjoy exactly the same general burglary deterrence effects from widespread American gun ownership. Part II of this Article looks at the differences between the behavior of American burglars and their cousins in other nations. Part III specifies the risks that American burglars face from various deterrents, including armed victims. Part IV details how burglars choose targets, while empirical data about burglary deterrence are analyzed in Part V. Part VI looks at what happens during confrontations between burglars and victims. Part VII compares and contrasts defensive firearms ownership with other anti-burglary strategies, such as guard dogs. Policy implications and network effects of firearms ownership are explored in Part VIII.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.004 |
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".