Examining Markets, Marketing, Consumers, and Society through Documentary Films
Bibliographic record
Abstract
Documentary film is over 100 years old and includes subgenres such as ethnography, historical film, docu-drama, propaganda, and advocacy videos. With numerous film archives, film festivals, special DVD issues of journals, inexpensive video recording and editing equipment, Internet distribution, and the phenomenal growth of archival Internet sites such as YouTube and Vimeo, there are now hundreds of millions of documentary films and videos available to the interested researcher. The author argues that the macromarketing field has greatly underutilized this vast resource and suggests examples of sources and uses for such material. The author also suggests some aids for acquiring critical visual literacy skills to inform such analyses. Just as we rely on our libraries and online access for books and print journals, we can readily do the same with documentary films. Such analytical projects can be presented as either video documentaries themselves, as text-based articles and books, or as multimedia combinations. Film, video, Internet, and television images arguably do more to influence public perceptions of marketing, consumption, and life than any other medium. There is thus a great opportunity to understand society through this window on the world.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".