Bibliographic record
Abstract
This is our final issue as co-editors of Complicity, and so we would like to begin our remarks with a brief expression of appreciation to members of the community who have assisted in preparing and reviewing manuscripts.In particular, we thank our Book Review Editor, Kristopher Wells, who always seemed to be a step ahead of us in the process.From the birth of Complicity following the first Complexity and Education conference in 2003 we have seen a steady increase in submissions and readership, reflecting the growth of interest in complexity theories by educators internationally.We wish the journal well and are delighted to see it move into the capable hands of a strong editorial team: Deborah Osberg (Editor-in-Chief), Bill Doll (Associate Editor), Donna Trueit (Associate Editor), and Darren Stanley (Book Review Editor).As we considered topics for this final editorial piece, one issue kept pressing itself into our awarenesses-namely, the changing landscapes of possibility and what these might mean for education and educational research.For the most part, discussions of the topic seem to be organized around a complexivist sensibility (often implicitly) and emergent technologies (usually explicitly).As has been noted by many commentators, the word technology tends to be popularly understood in terms of physical tools and machines.Occasionally language, mathematics, and other areas of human competence are
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".