Professeur émérite des Universités
Bibliographic record
Abstract
visiting for two years at the University of Toronto. I was then involved in "quantitative geography", quite new and popular at the time. But my curiosity went beyond matrix calculus or Volterra-Lotka distribution, into philosophy and particularly into Hegel's logic. I got in touch with Gunnar: both, we partook a deep interest in philosophy. It was the beginning of a friendship which saw us moving back and forth, with our graduate students, for philosophical seminars between Ann Arbor and Toronto. I still keep happy remembrances from this time past. Hopefully, this small paper might recall somehow our friendly meetings and our passion for philosophical thinking. Since then, my criticism of “territories ” and of French “Aménagement du territoire ” has gone further ahead. It has lead to an International colloquium in 2007 and to the publication of a book on The enemies of Paris. National and Regional Planning in France go, since half a century, under the name of "Aménagement du Territoire". Nobody seems to have noticed the pre-eminence given in this name to “territory ” over men. The role of planning is defined, in this way, as ensuring equality (in public equipment, activities, development...) between different pieces of land, not between different households. French media and politicians keep lamenting about territorial inequalities. When population, however, is concentrated in cities and resources are scarce, equality between
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.368 | 0.195 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".