Making Punishment Memorialization Pay? Marketing, Networks, and Souvenirs at Small Penal History Museums in Canada
Bibliographic record
Abstract
Existing literature on the commodification of punishment has yet to examine small penal history museums or related issues of tourism marketing, networking, and souvenirs. Bringing this literature into conversation with tourism studies, we examine how penal history sites attempt to attract visitors and generate revenue to sustain their operations. Drawing on findings from a 5-year qualitative study of penal history museums across Canada, we argue tourism operators use three strategies for the marketing of commodified punishment: authenticity, historical specificity, and exclusiveness. Our findings also indicate that networking between these sites is underdeveloped and that the souvenirs sold to visitors are an important source of museum funding. Overall, we show that the concepts of marketing, networking, and souvenirs can comprise a key conceptual framework for examining consumption in small tourism enterprises in Canada and internationally. Our findings also raise questions about how to theorize and investigate museum management, solvency, and profitability in the penal and dark tourism sector.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".