The Future of Scholarly Journal Publishing among Social Science and Humanities Associations
Bibliographic record
Abstract
The study described in this report grew from recommendations for an investigation into journal economics by the National Humanities Alliance Task Force on Open Access and Scholarly Communications. Since experiments are underway to understand and enable a range of options for a shift to an open access (OA) business model for publishing some scientific, technical, and medical (STM) journals, the question arises, Do these same options exist for a similar shift within humanities and social science (HSS) journals? Findings are reported from detailed analyses of the publishing economics, including all revenues and all costs, of eight flagship US journals across a number of different HSS disciplines. Using actual business information from their association publishers for each of the years 2005, 2006, and 2007, these findings clarify that for this sample of journals, an OA business model based only on revenue from the research article author or producer would not be sufficient to sustain these journals. The research articles published in these journals were longer than typical STM journal articles, and the percentage of non-article content (e.g., book reviews and other scholarly content) was greater. Information-gathering tools and methodologies that enable like-for-like comparison of journal revenues and costs were developed and are described in the report. As an initial in-depth business review of a sample of HSS journals, the report further clarifies some of the key differences between STM and HSS journals, articulates recent journal performance, makes tentative conclusions based on this sample, and proposes further questions that need to be answered to support a shift to OA business models that are sustainable across HSS journal publishing.
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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 | 0.048 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.051 | 0.036 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".