Use of politeness strategies in signed open peer review
Bibliographic record
Abstract
Scholarly peer review is a complex collaborative activity that is increasingly supported by web‐based systems, yet little is known about how reviewers and authors interact in such environments, how criticisms are conveyed, or how the systems may affect the interactions and use of language of reviewers and authors. We looked at one aspect of the interactions between reviewers and authors, the use of politeness in reviewers' comments. Drawing on B rown and L evinson's (1987) politeness theory, we analyzed how politeness strategies were employed by reviewers to mitigate their criticisms in an open peer‐review process of a special track of a human‐computer interaction conference. We found evidence of frequent use of politeness strategies and that open peer‐review processes hold unique challenges and opportunities for using politeness strategies. Our findings revealed that less experienced researchers tended to express unmitigated criticism more often than did experienced researchers, and that reviewers tended to use more positive politeness strategies (e.g., compliments) toward less experienced authors. Based on our findings, we discuss implications for research communities and the design of peer‐reviewing processes and the information systems that support them.
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 | 0.062 | 0.348 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".