Bibliographic record
Abstract
Explanation Through ConversationJoe:George, I would like to thank you for arranging this arts-based Research Special Issue of ajer.Although you know that I have been an advocate of the arts in general and arts-based research and the arts as a learning medium specifically, my major focus has never been the arts for arts' sake.I envision the arts as a tool, and like any good steward its purpose is to serve.For me the arts have a major role to play in epistemological and research arenas.If the medium and content are intricately and intimately entwined as I believe they are, we must continue to push the textual boundaries of our research.I am pleased that the approximately 30 submissions did just that.In addition, I am delighted that we obtained funding to include a CD-ROM bound to the journal and that we decided to play with the editorial writing in a conversational form.We too must practice what we preach.
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 | 0.009 | 0.059 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.057 | 0.031 |
| Insufficient payload (model declined to judge) | 0.026 | 0.012 |
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".