Multiscalar Structures in Geography: Contributions of Scale Relativity
Bibliographic record
Abstract
Scale issues are very meaningful in geography, but nowadays nobody knows how to explain their ubiquitous existence theoretically. Fractality is not an accident for all geographical objects. The aim of this article is to demonstrate to what extent the theory of scale relativity (SR) can be used to solve the problem of geographic scales. With it, we can explain why fractal objects are everywhere. First, we summarize geographic scale position, followed by introducing all tools to understand SR with basic definitions, scale in cartography, how to measure a scale, scales in and from nature, and scale and theoretical geography. Second, we quickly describe the theory of SR. Indeed, it is an elementary geometry around first principles, characterization of scale variables, and scale laws. This article also aims to clarify why geographical objects are non-fractal, in a first calculus, and fractal, in a second calculus with the theory of scale relativity. Third, we will underpin this position through several geographic cases with a karstological example, two urban areas (Montéliard and Avignon), and a hydrographic network and contours of level lines (Gardons). All of them will be carefully analyzed with a fractal analysis. Therefore, we conclude that in this case we are well and truly within the framework of the theory of SR, depending on the results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".