Supporting Reviewing Reviews: How HCI Authors Handle Peer Reviews of Manuscripts
Bibliographic record
Abstract
Responding to peer reviews is a critical but under-supported stage of academic writing. Authors must interpret reviewer comments, infer underlying concerns, and coordinate revisions across teams. We report findings from interviews with 14 HCI authors that reveal how they engage in this interpretive and collaborative process. Authors distinguish between surface-level content and subtextual meaning in reviews, and often rely on intermediary documents to track issues, assign tasks, and develop response strategies. These documents support sensemaking, communication, and planning, but must be built manually. Our findings suggest that while interpretation of subtext remains a human judgment task, there are clear opportunities for interactive tools to support coordination, document linking, and traceability. We offer design implications for next-generation writing tools, including those powered by language models, that align with authors’ workflows and preserve their interpretive agency.
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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 | 0.330 | 0.696 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.050 | 0.036 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.025 | 0.042 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".