Bibliographic record
Abstract
In this chapter, O’Donnell considers the state of ‘revolutionary stasis’ in today’s scholarly communication by examining LISTSERV and the early online community as a case study. Beginning with a brief history of the early online community (with the rise of email and the LISTSERV mailing list distribution utility), O’Donnell goes onto distinguish two approaches to the design of online, email-based communities: to act as a computer-mediated representation of an existing academic form, as well as to treat mailing lists as an informal, conversational space. O’Donnell engages with Patrick Connor’s discussions on the importance of para-academic social practices over more formal scholarly elements and argues that we are looking for change in the wrong place – our work practices are changing not as a result of digital technology innovations replacing our previous methods, but by supplementing and building on them. O’Donnell argues that it is the expansion of informal channels that has revolutionised in-group para-disciplinary communications. The chapter ends with O’Donnell’s thoughts on where currently emerging innovations can lead us.
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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.051 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".