The Good, the Bad and the Ugly? The Dynamic Interplay Between Educational Practice, Policy and Research
Bibliographic record
Abstract
The notion of complexity — as in “education is a complex system” — has two different meanings. On the one hand, there is the epistemic connotation, with “Complex” meaning “difficult to understand, hard to control”. On the other hand, complex has a technical meaning, referring to systems composed of many interacting components, the interactions of which lead to self organization and emergence. For agents, participating in a complex system such as education, it is important that they can reduce the epistemic complexity of the system, in order to allow them to understand the system, to accomplish their goals and to evaluate the results of their activities. We argue that understanding, accomplishing and evaluation requires the creation of simplex systems, which are praxis-based forms of representing complexity. Agents participating in the complex system may have different kinds of simplex systems governing their understanding and praxis. In this article, we focus on three communities of agents in education — educators, researchers and policymakers — and discuss characteristic features of their simplex systems. In particular, we focus on the simplex system of educational researchers, and we discuss interactions — including conflicts or incompatibilities — between their simplex systems and those of educators and policymakers. By making some of the underlying features of the educational researchers’ simplex systems more explicit – including the underlying notion of causality and the use of variability as a source of knowledge — we hope to contribute to clarifying some of the hidden conflicts between simplex systems of the communities participating in the complex system of education.
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 | 0.062 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.012 | 0.098 |
| Scholarly communication | 0.041 | 0.050 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".