Valuing the Role of the Editor: Now and in the Future
Bibliographic record
Abstract
Reflecting on the last 30 years of the Journal of Electronic Publishing (JEP) as the current co-editors, we find ourselves tracing the role of the journal editor over time.It is difficult to reflect backward without making sweeping claims that may or may not be grounded in truth: that journal editing used to be more about gatekeeping (was it?)and ensuring rejection rates stayed high (for everyone?)and thereby preserving prestige (always?).What we can speak to with confidence, however, is our own commitment to editorial practice as curation and community building-to taking a values-based approach to editing this journal that balances inclusion with interest, collaboration with timeliness.We remain grateful to the editors who came before us and steered the JEP ship in such a way that we can enter this fourth decade of the journal with our value-laden commitment at the helm.JEP started off as an academic publishing trade-initiated, free, online journal, with an editorial focus on curation and experimentation.Colin Day (then director of the University of Michigan Press) started JEP in 1995 to "bring together all the interesting papers he had been reading and hearing presented about scholarly electronic publishing" alongside original refereed publications, interesting links, and news items.Yet the journal was also a testbed, a vehicle for experimentation to "test new ideas about the way an electronic journal might operate" (Day, quoted in Turner 1998).Judith Axler Turner, who took over from Colin Day as editor in 1997, further explored this in her first issue for JEP, asking editors of electronic-only, peer-reviewed scholarly journals "to write articles explaining what they were doing, why, and how it was working" (1998).For Day and Turner, the journal was primarily aimed at publishers, in addition to librarians, academics, and the generally interested; yet they saw what they were doing as a "broader, more intellectual approach to the universe of issues that cluster around the topic of electronic publishing."As Day explained, "we do not seek how-to articles but we do seek how-to-think-about articles" (quoted in Turner 1998).Turner and Maria Bonn, JEP's editor in the mid-2010s, continued this focus while exploring "many of the urgent topics of the contemporary publishing conversation"-mainly in relation to the economics and politics of publishing while experimenting with the "expressive
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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 | 0.064 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.063 | 0.061 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".