Bibliographic record
Abstract
The development of “open” academic content has been strongly embraced and promoted by many advocates, analysts, stakeholders, and reformers in the sector of higher education and academic publishing. The two most well-known developments are open access scholarly publishing and Massive Online Open Courses (MOOCs), each of which are connected to disruptive innovations enabled by new technologies. Support for these new modes of exchanging knowledge is linked to the expectation that they will promote a number of public interest benefits, including widening the impact, productivity, and format of academic work; reforming higher education and scholarly publishing markets; and relieving some of the cost pressures in academia. This article examines the rapid emergence of policy initiatives in the United Kingdom and the United States to promote open content and to bring about a new relationship between the market and the academic commons. In doing so, I examine controversial forms of academic unbundling such as open access megajournals and MOOCs and place each in the context of the heightened emphasis on productivity and impact in new regulatory regimes in the area of higher education.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".