Navigating the heavy seas of online publishing: Reflections on ten years editorship
Bibliographic record
Abstract
Articulo – Journal of Urban Research celebrates its 10th anniversary! To celebrate this milestone, the current editors discuss the numerous changes and challenges related to publishing a peer-reviewed online journal. Since 2005, Articulo has progressively become more international, more professional, and more specialized than its initiators ever dreamed it could be. In ten years, the rather confidential publication has progressively become a journal read by 9,000 people every month. Our editors, who used to work side by side, now live 6,000 km apart. In the midst of often tumultuous change, what hasn’t changed is our commitment to the highest quality of publication and our conviction that knowledge – especially if funded by public money – should be universally and freely accessible. Rigorous peer-review and open access are the two principles on which the journal will navigate the often rough seas of online publishing in the future.
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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 | 0.153 | 0.273 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.019 | 0.025 |
| Scholarly communication | 0.070 | 0.048 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.019 | 0.035 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".