The 2010 Winter Olympics: A Mixed-Methods Investigation of the Hotel Industry and Tourism in the Demographic Clusters metro–Vancouver versus the alpine–Resort Whistler
Bibliographic record
Abstract
In this thesis, applying an innovative postmodern equal-weight/sequential QUAN→PHEN Mixed-Methods Phenomenological Research (‘MMPR’) approach to study an Olympics’ impact within its two-cluster socio-demographic footprint forms its main contribution to knowledge. Facilitating between-methods triangulation is a novel eclectic pragmatic approach that is used to capture the richness of thematic data flowing from in-depth, open-ended interviews with most – 62 in all – senior Hoteliers spread evenly between distinct urban Metro-Vancouver and rural alpine-Whistler, whilst concurrently capitalizing on the availability of a unique BC Stats proprietary micro-municipal-level secondary data source, i.e., British Columbia’s ‘Additional Hotel Room Tax’ (‘AHRT’). Typically, traditional mono-method-positivist neo-classical economic syntheses are used to quantify an Olympic Games’ ex-ante or ex-post impact. This study’s findings confirm that such syntheses attempts, at the micro-municipal level, lead to inevitable dead-ends. At a sub-national level of micro-granularity, using available economic models is an impossible task due to the insurmountable practical problem of complete lack of, or paucity, of data. When applied to assess mega-events, such modelling is shown to lack credibility; models are insufficiently comprehensive or its users consciously engage in ‘shenanigans’ by force-fitting input/output to produce pre-ordained outcomes for political expedience and meeting agency interests. The ‘MMPR’ approach acknowledges and respects the established and ‘current-thinking’ paradigmatic epistemological and ontological perspectives. ‘Hotel Activity’, measured via ‘AHRT’, is substituted as a ‘Proxy’ for ‘Tourism’ following empirically establishing these three variables as highly correlated. Prevalent academic findings of negative impacts from Winter Olympics are not borne out. Phenomenological issues of ‘illusory correlations’ and ‘data saturation’ are addressed.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".