Bibliographic record
Abstract
‘For me, formulating a paper is always personal and sending it in for review always entails a sense of vulnerability. In a sense, I consider it to “expose” myself. Particularly in relation to the work I’ve been doing lately, where I find it important for our field to also engage in a form of self-criticism and investigate what we take for granted or assume when doing feminist/progressive work, it has happened that I have gotten harsh critiques in reviews, that I have not always found fair in relation to the argumentation put forward in the paper submitted, but rather protective in relation to certain positions in the field. This is certainly not to suggest that I have not also received very constructive and highly deserved critique, but the traditional double-blind process provides little room to explain oneself, little room for dialogue and lots of room for being “suspicious” about each other as author/reviewer. I therefore highly appreciated the more “developmental” or dialogical reviewing process set up through the conference/for this special issue. I found it provided space to be more personal and even vulnerable in responding to the reviews, and that the process ultimately allowed for a more direct and “honest” dialogue, because the need to protect certain positions was toned down in favor of trying to understand each other’s arguments. So, for me and for this paper, this review process had strong merits, and I really appreciated the feedback I got, which made feel that I wanted to better my paper and clarify my argumentation rather than defend myself after feeling hurt.’ (communication by email on 2025-04-28)
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 | 0.272 | 0.561 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.022 | 0.025 |
| Scholarly communication | 0.035 | 0.025 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.061 | 0.084 |
| Insufficient payload (model declined to judge) | 0.018 | 0.017 |
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; the direct Gemma label and the distilled Codex classifier 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".