A sport-oriented place branding strategy for cities, regions and countries
Bibliographic record
Abstract
Purpose How could a city, a region or a country succeed in its attempt to use sport to (re-)define, position and promote itself? Consequently, what do jurisdictions and brand managers need to consider when using sporting events as a leverage to market themselves abroad? The paper aims to discuss these issues. Design/methodology/approach This paper draws from a combination of an extensive literature review and secondary data collection in order to build a conceptual framework, entitled the “diamond” of place branding through sport. Findings Managers and politicians of cities, regions and countries should espouse a holistic approach when developing their place branding strategy through sport. This holistic approach can be articulated around four dimensions: sport, economic, commercial and social. Research limitations/implications Drawing mainly from a literature review, with the support of concrete examples, this is a first step within the confines of an exploratory research. A future study could analyze the specific cases of jurisdictions and how these fit within the conceptual framework articulated in this paper. Originality/value A place branding strategy through sport should be translated into a socio-economic legacy, with private and public benefits for the community. Ultimately, place branding through sport is one of the components of the overall place branding strategy of a jurisdiction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".