Peer-Reviewer Round Table Response to Ted Riecken’s Scholarly Podcast, “Mapping the Fit Between Research and Multimedia: A podcast exploration of the place of multimedia within / as scholarship”
Bibliographic record
Abstract
Beginning with the question of blind peer review in the shifting landscape of multimedia publishing, and concluding with reflections on knowledge-creation in today’s academic culture, Riecken, Leggo, and Paré respond to Riecken’s podcast-article and reflect on the challenges of multimedia and other non-traditional forms of scholarship for the academy and for scholarly communication. Leggo and Paré were the peer reviewers for Riecken’s article, which is part of this same issue and can be listened to here: http://mje.mcgill.ca/article/view/9061 . Since they hail from the same vicinity, they convened an author-peer reviewer round-table discussion on the issues raised in writing and reviewing a multimedia article. We are pleased to share their conversation.
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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 | 0.016 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.095 | 0.075 |
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".