Understanding motivation and perception at two dark tourism attractions in Winnipeg, MB
Bibliographic record
Abstract
While visitation and interest in dark tourism sites has been growing in the last century, lit¡e specific research was conducted in the area prior to the mid-1990s.since that time, there has been increasing academic and media attention paid to this form of tourism.The purpose of this research was to understand motivation and perception at two dark tourism attractions in Winnipeg, MB (a ghost tour and a cemetery).Sharpley,s typology of dark tourism was used as a framework to investigate visitors' motives and satisfaction at two different attractions that offered differing types of experiences with death to visitors (i.e.accidental or purposeful supply).The influence of respondents' perceptions of the site(s) as part of their own heritage was investigated as a possible factor in motivation.ln general, several of the dark motives were rated fairly low as reasons for visiting both attractions.V/hile overall motive satisfaction was high at both attractions, satÍsfaction with dark motives was found to be largely unaffected by supply and only two differences were found between the two attractions.Three motive items crossed the attractions and were identified as a potential "dark experience" motivation.lt is possible that these three motives may form a triumvirate core of "dark" motivation in dark tourism.Perception of the attractions as part of visiiors' own heritage was not found to be lower amongst those who visited for "dark" reasons.Visitors that scored the lowest level of dark motivation were those that did not perceive the attraction as part of their own heritage.Based on the results of this study new insight was gained into the experiences of visitors to dark tourism sites, which in turn have theoretical and practical implications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".