Politeness Strategies in a Speech by Jordan B. Peterson about "How to be Articulate"
Bibliographic record
Abstract
Politeness is a concept in the area of pragmatics and conversational analysis in which the speaker considers several factors to be polite, including their relationship with the hearer, their age, the power they have over the hearer, the importance of their utterances, etc. Speeches from prominent figures provide rich sources of analysis on politeness, a vital concept in everyday communication. This study examines different realizations of politeness strategies. The researcher used Brown and Levinson's model (1987), categorizing the strategies into four main realizations (Bald on-record, positive politeness, negative politeness, and off-record). The data source has been a 15-minute video clip from Jordan B. Peterson – a prominent Canadian psychologist and author – about being articulate, for which a qualitative method was used. During the data analysis, first, the author watched the video clip to get the gist, then wrote its transcription to look for the types of strategies used by the speaker. The transcription was then re-evaluated by two teachers in the field of English as a foreign language (EFL)to assure its inter-rater reliability. The results showed that the speaker employed the four types of politeness strategies during the speech, among which negative and positive politeness, together with their realizations – 'do not presume/assume' and 'raise/assert common ground were the most dominant. Further research on known figures, in different contexts and with larger data is imperative to ameliorate the pragmatics knowledge of both teachers and students and enhance their interactions.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".