Human dimensions of peer review in information science
Bibliographic record
Abstract
ABSTRACT While peer review is a popular research subject in academia, including information science, most sources focus on the mechanics, benefits and shortcomings of the process. In departure from this trend, this poster will address the human dimension of peer review and its role as a community building instrument and a locus of relationship formation. The poster will look at how “negligent and unscrupulous reviewing can detrimentally affect” “a sense of community” (Dali & Jaeger, n.d.) and support this argument through a new analytical framework based on humanistic pedagogy. It will address the role of peer review in the changed information science landscape; present a positive outlook on peer review “as a privilege and an unmatched academic opportunity”; “examine in detail the elements of helpful and unhelpful reviews”; and provide “advice to authors on how to respond to reviews, especially the unhelpful ones” (Dali & Jaeger, n.d.). The poster should appeal not only to junior faculty and PhD students, but also to practitioners who aspire to publish and experienced authors involved in scholarly communication and grant reviewing.
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 | 0.036 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".