The Writing’s on the Firewall: Assessing the Promise of Open Access Journal Publishing for a Public Sociology of Sport
Bibliographic record
Abstract
The process of digitization has transformed the ways in which content is reproduced and circulated online, rupturing long held distinctions between production and consumption in the (virtual) public sphere. In accordance with these developments over the past fifteen years, proponents for open access publishing in higher education have argued that the (not yet absolute) transition from physical to digital modes of journal production opens up unprecedented opportunities for redressing the restrictive terms of ownership and access currently perpetuated within an increasingly untenable journal publishing industry. Through this article, I advocate that the sociology of sport community hastens to question, challenge and reimagine its position within this industry in anticipation of a reformed publishing landscape. The impetus for the paper is to ask not whether sociologists of sport should or should not publish open access, but rather as open access publishing inevitably comes to pass in some form, what say will the field’s associations, societies and members have in these changes, and how might they help invigorate a public sociology of sport?
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 | 0.074 | 0.261 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.018 | 0.041 |
| Scholarly communication | 0.039 | 0.040 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.006 |
| 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".