Bibliographic record
Abstract
SOCIAL REACTION TO DEVIANCE : AN EXPLORATORY STUDY The object of this exploratory study is to analyse social reactions to deviance by endeavouring to show some of the mechanisms of these re- actions. To do this, we have isolated deviant behaviour from its situational elements in order to study the fluctuations of reactions according to the nature of the deviance, and according to the categories of those reacting to the deviance. The instrument of measurement is a questionnaire which was administered to a random sample for pre-testing. This sample was drawn from the metropolitan region of Montreal. The data analysis is concerned with the degree of generality, consensus, consistency and contingency of reactions in terms of the nature of the deviant behaviour. The results showed the reactions to be surprisingly general. The regularity of the continuum drawn by the indices of contingency and those of strictly punitive consensus was remarkable. This exploratory study gives interested researchers information on crime-deviance continuums and social reactions to deviance. The continuum of social reactions is clearly defined, and this gives us reason to believe it would be possible to introduce an order of importance in the evaluation of social reactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".