Coached Peer Review: Developing the Next Generation of Authors
Bibliographic record
Abstract
PROBLEM: Publishing in academic journals is challenging for learners. Those who pass the initial stages of internal review by an editor often find the anonymous peer review process harsh. Academic blogs offer alternate avenues for publishing medical education material. Many blogs, however, lack a peer review process, which some consumers argue compromises the quality of materials published. APPROACH: CanadiEM (formerly BoringEM) is an academic educational emergency medicine blog dedicated to publishing high-quality materials produced by learners (i.e., residents and medical students). The editorial team has designed and implemented a collaborative "coached peer review" process that comprises an open exchange among the learner-author, editors, and reviewers. The goal of this process is to facilitate the publication of high-quality academic materials by learner-authors while providing focused feedback to help them develop academic writing skills. OUTCOMES: The authors of this Innovation Report surveyed (February-June 2015) their blog's learner-authors and external expert "staff" reviewers who had participated in coached peer review for their reactions to the process. The survey results revealed that participants viewed the process positively compared with both traditional journal peer review and academic blog publication processes. Participants found the process friendly, easy, efficient, and transparent. Learner-authors also reported increased confidence in their published material. These outcomes met the goals of coached peer review. NEXT STEPS: CanadiEM aims to inspire continued participation in, exposure to, and high-quality production of academic writing by promoting the adoption of coached peer review for online educational resources produced by learners.
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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 | 0.075 | 0.265 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.012 |
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".