Say ‘Yes!’ to peer review: Open Access publishing and the need for mutual aid in academia
Bibliographic record
Abstract
Scholars are increasingly declining to offer their services in the peer review process. There are myriad reasons for this refusal, most notably the ever-increasing pressure placed on academics to publish within the neoliberal university. Yet if you are publishing yourself then you necessarily expect someone else to review your work, which begs the question as to why this service is not being reciprocated. There is something to be said about withholding one’s labour when journals are under corporate control, but when it comes to Open Access journals such denial is effectively unacceptable. Make time for it, as others have made time for you. As editors of the independent, Open Access, non-corporate journal ACME: An International Journal for Critical Geographies, we reflect on the struggles facing our daily operations, where scholars declining to participate in peer review is the biggest obstacle we face. We argue that peer review should be considered as a form of mutual aid, which is rooted in an ethics of cooperation. The system only works if you say ‘Yes’!
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 | 0.116 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.088 |
| Scholarly communication | 0.048 | 0.035 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".