Marketing traditional and contemporary folklore: how microbreweries and community events process local legends and folklore in Québec
Bibliographic record
Abstract
This thesis examines the commodification of culture pertaining to re-imaginings of historical Québec, New France colonies, political rebellions, imagined communities, shared cultural traits, and romantic heroic associations as seen in beer product placement, advertisements and reenactments from theme-restaurants to organized community events. Because folklore is often used to sell products and tourism packages, these products become transmitters of the selected folk items whether they are legends, figures, or the knowledge of occupational folk trades, as seen and constructed by the population creating these products. While much of the folklore scholarship surrounding the uses and misuses of folklore in consumption focuses on concepts of “authenticity,” I focused the lens on how companies view utilizable items of folklore, from packaging to public relations, and how the selection and rejection of vernacular heritage is used for cultural pride and identity. This research challenges theories on legend dissemination in form as well as perceived “shared common traits” used in commodified objects. I conducted fieldwork using various methodologies, from individual session interviews to market focus groups and also included an online survey to examine the process of using folklore in selling products and how this influences and produces community events. These different approaches to collecting and analyzing data by combining the traditional one-on-one interviews in folklore with the focus group sessions and surveys found in marketing studies has proved not only useful but necessary when researching a hybrid form of folk-consumer studies. The outcomes of this research are relevant for business studies notably in how marketing models and their studied interpretations bring folklore perspectives in the use of targeted mass media planning.
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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 | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".