La méthode des scénarios en prospective
Bibliographic record
Abstract
Increasingly scenarios are used as an important component of long-term planning. But not all scenarios are equally valid and equally useful for the decision-maker. Defining a scenario as a "synthetic process which stimulates step by step and in a plausible fashion a series of events which eventually lead a system to a new state", this study examines two kinds of scenarios: exploratory, where the inquiry proceeds from the present situation to a future one, and normative, where the search proceeds from a desirable future to the present. For each type of scenarios three sets of theoretical problems are examined: 1) the role of values, which must be explicitely recognized and used as such; 2) the concept of causality, which in a scenario has to be dealt with differently than in an "ordinary" scientific research, 3) the problem of time and the need to break the linear conception of the link existing between events. Finally, the study examines a number of practical tools and criterias (coherence, interaction, …) with which to build and to judge scenarios.
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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 | 0.027 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".