Mapping the Landscape of Peer Review in Computing Education Research
Bibliographic record
Abstract
Peer review is a mainstay of academic publication -- indeed, it is the peer-review process that provides much of the publications' credibility. As the number of computing education conferences and the number of submissions increase, the need for reviewers grows. This report does not attempt to set standards for reviewing; rather, as a first step toward meeting the need for well qualified reviewers, it presents an overview of the ways peer review is used in various venues, both inside computing education and, for comparison, in closely-related areas outside our field. It considers four key components of peer review in some depth: criteria, the review process, roles and responsibilities, and ethics and etiquette. To do so, it draws on relevant literature, guidance and forms associated with peer review, interviews with journal editors and conference chairs, and a limited survey of the computing education research community. In addition to providing an overview of practice, this report identifies a number of themes running through the discourse that have relevance for decision making about how best to conduct peer review for a given venue.
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 | 0.451 | 0.567 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.023 |
| Science and technology studies | 0.027 | 0.053 |
| Scholarly communication | 0.067 | 0.044 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".