Bibliographic record
Abstract
Digitization and the rise of Open Access publishing is an important recent development in academic communication. The current publishing system exhibits challenges with cost, where many universities are forced to cancel journal subscriptions for economic reasons, as well as access, as scholars and the public alike often lack access to research published in paywalled subscription journals. Open Access publishing solves the access problem, but not necessarily cost problems. Universities and researchers are currently in a challenging, interstitial stage of scholarly publishing. Subscription journals still dominate scholarly communication, yet a growing imperative to fund and support Open Access alternatives also exists. Stakeholders, including faculty, university administrators, publishers, scientific funding institutions and librarians and governments alike currently strategize and fight for their professional and economic interests in the broader publishing system. Four main trends are suggested that will characterize the future of scholarly publishing: 1) antagonism with scholarly associations; 2) changes and innovations to peer review; 3) Scientific/Intellectual Movements around Open Access 4) publishing and new professional niches in the publishing landscape. This article suggests potential trajectories and outcomes for these various conflicts over the costs and benefits of academic publishing.
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 | 0.045 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.041 | 0.035 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".