OJS Implementation and development of the Scientific Journals Site of the School of Humanities and Education Sciences of the Universidad Nacional de La Plata
Bibliographic record
Abstract
This article describes how the journal site of the School of Humanities and Education Sciences at the Universidad Nacional de La Plata was implemented and developed, so that our experience may be useful for anyone embarking on a similar undertaking. We first review the experience of the School in terms of scientific journal publication and the tasks performed by the Library to help its visualization. Secondly, we mention the work of the Under-Secretariat of Publication Management and Dissemination (PGEyD; its acronym in Spanish) of the School to make launching the site a reality. Special reference is made to software customization, massive information upload to the system (users and previous issues), and the procedures that enable the semi-automatic inclusion of the site content in the institutional repository and in the Web catalogue. Then, we discuss the work that is being carried out in connection with editors’ training and support, and the results obtained after one year of labour: the creation of 10 journals, the migration of the entire works of four titles and the inclusion of 25% of the contributions published in the journals edited by FaHCE. Finally, we point out a series of challenges that the Under-Secretariat has set itself to improve the site and to optimize intra- and inter-institutional workflow.
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".