Bibliographic record
Abstract
The increased visibility of complexity in the social sciences has raised questions about the ability of complexity theories to address political concerns.Many of these concerns are legitimate, particularly where complexity is portrayed as a superior naturalistic metaphysics of "life" which comes complete with a set of metaphors that can be used to legitimate certain social arrangements.In response to an article by Kevin Kelly in which complexity is portrayed in this way, Steve Best and Douglas Kellner (1999) rightly point out some of the shortcomings associated with this use of complexity and remark that this "uncritical approach to political realities and social power" is the "Achilles heel of complexity theory" (p.155).However, while I have no problem with Best and Kellner's assessment of this genre of complexity research in the social sciences, it is important to be clear that the genre is one that many who use complexity in the social sciences are themselves critical.Nevertheless, the objection raised by Best and Kellner a decade ago is relevant in that in education there is still very little work that draws on complexity to address education's political concerns and in this regard complexity's potential to be critical in an educational context is largely overlooked.In this issue of JCACS we bring together seven papers which were initially developed for a symposium entitled Complex Criticality in Educational Research (presented in the Complexity SIG of AERA in April 2007).The symposium aimed to address the perceived lack of a "political
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.068 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".