Bibliographic record
Abstract
For decades there has been pressure on those working in academia to engage more seriously with the publics they serve (Woodward 2009). Steps have been taken in this direction, see the rise of the public humanities as a movement (Wikipedia n.d.)—the international transition from closed, for-cost publishing to open access research (Suber 2012) and the establishment of public engagement priorities (Gibson 2009). Circumstances and opportunities for academic collaboration with wider communities have only improved with the rise of networked technologies and the resultant de-centering of authority in regard to who is considered to be an expert on any given subject. Narrowing down to the humanities, in particular, the development and international establishment of digital humanities (DH) as a robust and vibrant field of study has opened further doors; those in DH often have the proclivity, skills, and aptitude for working with colleagues across disciplines and outside of traditional academic institutions (Brown 2016). The prerequisites are in place for socially creating knowledge, collaborating across groups, and engaging with publics in more comprehensive and sustained ways. In what ways are academics and those who are academic-aligned taking up this form of more open, and more social, scholarship?
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.083 | 0.023 |
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".