“Lights, camera, action...!” Marketing film locations to Hollywood
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it