Auditing Social Science and Humanities Journals: The View of an Editor in a Malaysian Research University
Bibliographic record
Abstract
It is common practice for a university to have many journals located within different schools and faculties in order to help young researchers publish their work and gain confidence in their writing abilities. The focus of this paper is with the journals that are not listed in databases and cater only to academics within a school to serve as an avenue for publication. When the National University of Malaysia was accorded research university status recently, publications and research became an important indicator of the performance of the university. This led to a new demand for publications in indexed journals and for increasing citations. Hence, it was timely to conduct an ‘in-house evaluation’ of journals within the university, focusing particularly on the social sciences and humanities journals. An evaluation was conducted using the basic criteria for journals included in the database Scopus, and measures were then proposed to improve the journals. This exercise was meant to help the journals that had a long publishing history to rise to the challenges of being scholarly journals in an era of competitiveness, databases and indexes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.026 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".