“Lights, camera, action...!” Marketing film locations to Hollywood
Bibliographic record
Abstract
Purpose The purpose of this paper is to understand and identify the marketing strategies and specific promotional tools used by film commissions to attract the production of films and television. Design/methodology/approach The paper involves in‐depth interviews with film commissions worldwide and a content analysis of their promotional materials. Findings Film commissions employ three key strategic marketing approaches when promoting their locations to film producers – product differentiation, service differentiation, and cost advantages. They use six main specific promotional tactics – advertising, sales promotions, joint promotions, public relations, online marketing, and direct marketing and personal selling. A model explaining the relationship between film commissions and film producers involving these strategies and promotional tools is suggested. Research limitations/implications The marketing of film locations is under‐researched and has to be further addressed in the marketing literature. Future research can seek to identify the specific marketing activities that will lead to success for the marketing of film locations. Practical implications Examples of the best marketing practices amongst film commissions are highlighted. Originality/value This is an original contribution in that it is the first academic paper to address the marketing of film locations. It will be of significant value to film locations seeking to attract production to their locations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.089 | 0.009 |
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".