L’expertise stratégique face aux développements de l’intelligence articielle
Bibliographic record
Abstract
In the first section of this paper, the author tries to demonstrate how the increasing importance of modelisation/simulation reveals the existence of a crisis in strategic thought, seen as a crisis in the management of complexity, even more so as a crisis regarding the fundamental concepts of strategy, and regarding its claim even (as the " triumph of the means over the end " ) to tell how the world should be managed and what must be its destiny. At the same time, it is suggested that the dominance of " technolanguages " is growing, that the various attempts to overcome this crisis through the use of " artificial intelligence " are extremely promising, provided however that we agree to " a criticism of the strategic time-space ". In the second section, the author deals with the main problems and constraints linked with the conversion of strategic expertise into information processing languages and recommends that research be done along five axes : an update of the " fundamental connectors ", a kind of synapse in the strategic thinking, a study of the " attributes " and a setting up of such elaborate typology as " linguistic atoms ", and finally analyses of " contexts " and " key questions ".
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".