L’expertise stratégique face aux développements de l’intelligence articielle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 ".
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 it