L'Interprétation Comme Reparation et Comme acte Menaçant pour la Face (FTA): (Im)poli envers qui?
Bibliographic record
Abstract
In this article, we use the theoretical frameworks proposed by Brown and Levinson (1978) and by Kerbraat-Orecchioni (1996, 2002) to reveal hybrid politeness strategies in the linguistic behaviour of African interpreters who figured in novels of the colonial period. The interpreters did not limit themselves to translating remarks that threatened the face of their listeners. They did nol hesitate to reprove those who offered rude responses, which they refused to translate in any case (reproval and refusal to translate both functioning as acts of linguistic impoliteness); nor did they hesitate to alter these remarks in order to make them more acceptable, less offensive, and less damaging for the face of their recipients (such transformation functioning as an act of reparation [Gaffman 1973], and equating to an act of politeness). That the same utterance is analyzed as an act of impoliteness (from the standpoint of the speaker) and as an act of politeness (from the standpoint of the hearer) reaffirms the nature of politeness, underscoring the role of point of view in the perception of the speech act. One might well ask: Polite (or impolite) for whom?
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".