Réflexions sur l’émergence d’une méthodologie de la planification prospective
Bibliographic record
Abstract
The recent work of a few thinkers in the United States and abroad shows with growing clarity that the idea of planning is beginning to crystallize, to have coherence and cohesiveness. This is an exciting as well as important development, especially if one remembers that the writers in question often approach the subject from quite radically differing angles of vision, from different backgrounds and from divergent intellectual commitments or personal biases. If, despite this, a convergence has become noticeable, it should be possible to think that beyond residual idiosyncracies of language and style, a planning methodology is emerging which appears capable of weaving manifold strands together into a common body of knowledge and application. For the moment, such a methodology remains open-ended. It has not yet been formalized into doctrine, and it is possible, and perhaps to be hoped, that it will remain this way. Nevertheless, its overall configuration, its main concepts and phases are now sufficiently general, so that one can describe and discuss them without reference to specific cases. My aim in these pages is to do precisely that.
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 | 0.065 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.063 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".