Bibliographic record
Abstract
Abstract A variety of forms serve as responses to thanks in Present-day English, albeit infrequently. Such responses minimize the debt incurred by the thanker and serve purposes of negative politeness. The history of responses to thanks has received only brief attention ( Jacobsson 2002 ; Jucker 2020 ; Taavitsainen and Jucker 2020 ). Most of the contemporary responses to thanks (e.g., no problem and you bet) are of quite recent origin. Those that “express pleasure” (the pleasure was mine) appear in the late-nineteenth century, while those that express “verbal acknowledgment” (all right, okay) appear in the twentieth century. The increase of minimizing responses is consonant with a trend toward negative politeness, while the loss of the deferential forms found in Early Modern English (your humble servant) reflects the rise of camaraderie politeness. Responses to thanks have also undergone “attenuation” ( Jucker 2019 ), evidenced by the appearance of short forms (welcome), the rise of verbal acknowledgment types, and the increasing use of such responses as conversational closers.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".