Study of Relation and Condition of Regional Industry Clusters on the Niche Theory and Model UNE ÉTUDE SUR LA RELATION ENTRE DES GROUPEMENTS RÉ GIONAUX INDUSTRIELS SOUS LA THÉORIE ET LE MODÈLE DE LA SUPERPOSITION D'ADAPTATION ÉCOLOGIQUE
Bibliographic record
Abstract
Regarding of the similarity between regional industry clusters and biologic species, and referencing the niche theory of biology, this paper analyzes the characters and rule of regional industry clusters, and provides the niche model of industry clusters, and then a case study was done to two industry clusters, which produces similar products. Niche model provides a new method and tool for the study of regional industry clusters, a different angle and thinking for the development and programming of regional economy, and a reference to the decision of economic management. Keywords: regional industry cluster、niche model、niche overlap、niche shift、standard parts Resume: Cet article se base sur la similarite des especes ecologiques et des groupements regionaux industriels, en utilisant la theorie de la superposition des adaptations ecologiques, il analyse la regle et des caracteres des groupements regionaux industriels, et bâtit le modele de la superposition d'adaptation ecologique pour les groupements regionaux industriels, et puis il fait une etude d'exemple sur deux groupements regionaux industriels qui ont des produits similaires. La methode de cette etude offre un nouvel instrument pour les recherches des groupements regionaux industriels, une nouvelle option ou idee au plan du developpement regional economique, et aussi une consultation a la politique de la gestion d'economie. Mot-cle: des groupements regionaux industriels, le modele d'adaptation ecologique, la superposition des adaptations ecologiques, le deplacement des adaptations ecologiques, standard parts
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".