Bibliographic record
Abstract
This thesis builds a public goods case for government intervention in the academic journal market. Synthesizing information from interviews with the existing quantitative and qualitative literature accomplishes this goal. The cost of doing business in the academic publishing market has steadily risen over time. In response, an “open access” (OA) movement has formed. Members of the movement argue that making academic research freely accessible to anyone with an Internet connection is the ideal way to control these costs. Others, however, are satisfied with the status quo. Determining who pays what price to allow free access has become increasingly important. National open access initiatives could be implemented without government aid if universities and academic libraries worked together; however, a collective action problem prevents cooperation. The government has tools that could be used to help these stakeholders transition to an open access status quo.
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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.084 | 0.041 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 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".