Bibliographic record
Abstract
Despite the framing of open access (OA) as a progressive movement that challenges neoliberalism and champions the public good, academic labour is often left out of these analyses (Eve, 2017). In a bid to liberate academic labour from the neoliberal hands of commercial publishing, advocates of OA have argued that making scholarly work “free” can help to establish an academic commons (de Rosnay, 2021). However initiatives to mandate OA in academia like “Plan S” set the stage for academic labourers to be compelled to give up rights to their intellectual property (Frantzvag & Stromme, 2019). In this essay I argue that the broad acceptance of OA as the liberatory savior of academic publishing is misguided, as it obscures the right-wing libertarian roots of the movement and would see academics voluntarily alienate themselves from their labour (Golumbia, 2016). Drawing on Golumbia’s (2016) Marxist reading of the political economy of OA, I argue that devaluing academic labour by characterizing it as unproductive and immaterial negates the abstract labour that produces scholarly works. Undoubtedly, libraries have an important role to play in the OA “revolution” (Burns, 2018), although not as assenting boosters but as critical voices that advocate for the rights of workers.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.036 | 0.026 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 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; 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".