Territorial Development at the Crossroads of Attractiveness and Sustainability
Bibliographic record
Abstract
An abundant literature in spatial planning, economic geography and regional science has focused on territorial attractiveness. However, most literature does not sufficiently integrate recent research challenges induced by sustainable development. The latter issue is likely to modify profoundly the locational determinants of economic activities and the mechanisms that explain the ability of territories to attract economic activities. Consequently, a novel approach articulating the necessity of integrating the concept of sustainable development and territorial attractiveness is pertinent. The purpose of this study is to show to what extent an evidence-based analysis of sustainable development goals affects the classification of countries according to their level of development and their potential to move to a higher ranking of their performance. On the basis of panel data on 52 countries, monitored over the last ten years, and through the explicit consideration of "proxy" variables of sustainability, attractiveness, and economic growth associated with measurable indicators, the paper seeks to assess the main basic trends, to develop a typology according to the main new strategic orientation, and to analyse rank order changes in different homogeneous groups of countries. The results confirm the idea of a general two-dimensional and dichotomic trend towards (or against) sustainability and attractiveness and employment prospects. Our study confirms the widening divide between countries in terms of their socio-economic and environmental policies.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".