Making Roubaix More Attractive
Bibliographic record
Abstract
The purpose of our study is to show how the city of Roubaix could rebuild its attractiveness. To that purpose, the offer and competitive advantages of the city were studied with the help of the Porter’s Diamond and CERISE REVAIT® models which are considered as tools for measuring attractiveness. A case study method has been chosen to carry out this study. The historical and documentary approach, together with the thematic analysis of interviews were used used to showcase the attractiveness through the study of the competitive advantages of the city. Findings show that attractiveness constitutes an indisputable link between Porter’ s Diamond and CERISE REVAIT® models, because they allow us to study the offer in its entirety, measure its attractiveness and bring to light the competitive advantages. It is stated that the art and culture sector is the most attractive and competitive in this territory and contributed to its development as urban tourism destination. This sector has been strongly supported by the public authorities and remains symbolic of Roubaix.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".