The Intelligence of Complexity: Do the Ethical Aims of Research and Intervention in Education Not Lead Us to a New Discourse "On the Study Methods of our Time"
Bibliographic record
Abstract
To better appreciate the contribution of the ‘paradigm of complexity’ in Educational sciences, this paper proposes a framework discussing its cultural and historical roots. First, it focuses on Giambattista Vico’s (1668-1744) critique of René Descartes’ method (1637), contrasting Cartesian’s principles (evidence, disjunction, linear causality and enumeration), with the open rationality of the ‘ingenium’ (capacity to establish relationships and contextualize). Acknowledging the teleological character of scientific inquiry (Bachelard) and the inseparability between ‘subject’ and ‘object’, the second part of the text explores the relevance of ‘designo’ (intentional design) implemented by Leonardo da Vinci (1453-1519) in order to identify and formulate problems encountered by researchers. Referring to contemporary epistemologists (Bachelard, Valéry, Simon, Morin), this contribution finally questions the relationships between the ‘ingenio’ (pragmatic intelligence), the ‘designo’ (modeling method) and ethics. It proposes one to conceive the paradigm of complexity through the relationships it establishes between (pragmatic) action, (epistemic) reflection and meditation (ethics).
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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 | 0.181 | 0.187 |
| 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 | 0.189 |
| Scholarly communication | 0.026 | 0.041 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".