A Survey of Social Science Journal Editors for Behind-the-Scenes Data on the Publication Process
Bibliographic record
Abstract
Conducting and publishing research is at the heart of the academic social scientist's job. Understanding the publication process is critical for any scholar looking for a successful career. The current study draws on survey data from 117 editors of social science journals to identify how editors experience their jobs, how manuscript reviews are processed, and what aspects of editors, journals, and manuscripts are most important to editors' publication decisions. Results suggest that editors relied on their editorial boards and associate editors to do reviews and give advice, that the greatest challenge editors faced in dealing with manuscripts was slow reviewers, and that rarely did editors face allegations of plagiarism or have to deal with inappropriate reviews they did not want to send to the author(s). Quality of writing and strength of findings are the most influential factors in journals' acceptance rates.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.359 | 0.718 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.026 | 0.075 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.131 | 0.112 |
| Open science | 0.023 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".