Epilogue: Designing and Performing the Editorial Assemblage
Bibliographic record
Abstract
In this paper, we report on our experiences as editors using AI in the review process. During the editorial process for Volume 95 of Research in the Sociology of Organizations on Algorithmic Organizing, we explored the benefits and challenges that arise when AI is incorporated into the editorial assemblage. In collaboration with our authors and Emerald Publishing, we used generative AI tools to augment the review process. To tease out the differences between a “regular” review process and ours, where AI was part of the editorial assemblage, we formulate tentative expectations for the future of reviewing. Specifically, we claim that integrating AI successfully in the editorial process hinges upon design and execution decisions at multiple levels and at multiple moments. Ultimately, and in light of rapid technological developments that are already changing the nature of editorial work, the creative, dynamic, and thoughtful design of editorial algorithmic assemblages can engender practices that enrich both the quality and enjoyment of academic research.
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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 | 0.021 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.067 | 0.035 |
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".