Food Clusters and Creative Tourism Development: A Conceptual Framework
Bibliographic record
Abstract
This paper examines food clusters as contributors to tourism development from a creative economy perspective. A conceptual framework is proposed that emphasizes four interdependent determinants and four facilitators that underpin the formation and operation of creative food clusters. The conceptual framework and its application to the Stratford case study site highlights the resources required as well as the place branding processes needed for food cluster development. Emphasis is given to the importance of partnerships between the public and private sectors as well as strong leadership in facilitating stakeholder collaboration and communication. The introduced conceptual framework is intended to act as a guide for those interested in pursuing a culinary tourism-focused creative economy strategy. Keywords: food clusters; creative economy; stakeholder collaboration; place branding; tourism development ------------------------------------------------------------- Cet article examine les regroupements alimentaires qui contribuent au developpement du tourisme dans une perspective de creativite economique. Un cadre conceptuel est propose pour mettre l'accent sur quatre facteurs interdependants et quatre facilitateurs qui soutiennent la formation et le fonctionnement de regroupements alimentaires creatifs. Le cadre conceptuel et son application a l'etude de cas Stradtford, sur site, met en avant les ressources necessaires ainsi que les processus de strategies de marque en place requis pour le developpement des regroupements alimentaires. L'accent est sur l'importance du partenariat entre les secteurs publics et prives ainsi que sur un fort leadership dans la facilitation de la collaboration et de la communication des parties prenantes. Le cadre conceptuel introduit est concu pour servir de guide pour ceux interesses dans la poursuite d'un tourisme culinaire-axe sur la strategie economique creative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".