Bibliographic record
Abstract
Most of the Linguists who have recently studied insult recognise (more or less explicitly) that it cannot be dealt with in a strictly semantic fashion, as pragmatic factors are central to the issue (see for instance Fisher, 1995; Siblot 1995; Lagorgette 2002, 2003; Rosier et Ernotte 2001, etc.). This study focuses on the sociopragmatic, interactional dimension of insults.Our reflection covers on the one hand a very social point of view aiming at spelling out the perception of insults by speakers of Quebec French; our corpus shows that utterances containing either form “insulter” and “insultant” refer more often to actions than to words. On the other hand, a more pragmatic point of view is developed, showing how insulting forms function within conversational contexts. The absence of an answer when axiological forms are used in family interactions (the items belonging therefore to an interzone between ritual and personal insults) leads to reconsider the status of such exchanges. We propose that it would be theoretically productive to think of insults as a zone within a continuum going from mockery to reproach and accusation (Laforest 2002), which would enable one to understand the different meanings associated by speakers to the feeling of “being insulted”. A further advantage would be that threats (as perceived) as a social act could be measured on a scale of intentions rather than on that of vulgarity.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".