Bibliographic record
Abstract
In the last four issues of Complicity, Bill, Donna and I have focused the content of our editorials on what it means to edit or contribute in other ways to a journal of complexity in a manner that does not contradict the "ethos" of complexity, whatever that is. In this regard we have been discussing the merits of a conversational approach in academic journals, and in particular the role of conversation in "enlarging the space of the possible around what it means to educate and be educated" 2 and in unsettling the power dynamics of the "gatekeeper" role. We have argued that a conversational approach in academic journals enables journal editors and reviewers to (i) move from a "policing" to a "facilitating" mode of "gatekeeping," (the former attempts to keep unauthorized ideas out, the latter invites unauthorized ideas in) and that (ii) this mode of gatekeeping 3 is crucial for enlarging the space of the possible. 4
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 | 0.027 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.105 |
| Scholarly communication | 0.029 | 0.052 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".