What is tourism for? Growth, sustainability and regeneration in Canadian tourism policy
Bibliographic record
Abstract
A better understanding of how tourism’s purpose is conceptualized and operationalized in tourism policy is urgently needed to support critical conversations, policy actions and business development that reorients post-Covid 19 tourism culture towards positive social, economic and environmental contributions. This longitudinal analysis of tourism policy examines how language and linguistic devices are deployed to maintain and transform understandings of tourism’s purpose and as reflections of the cultural values which shape tourism policy. Federal level Canadian tourism policy documents from the 2011 and 2019 are examined using Corpus Linguistics computer-aided text analysis to quantify the frequency, dispersion, clusters, and collocates of the terms growth, sustainable/sustainability and regeneration as well as Critical Discourse Analysis to interpret and contextualize the discursive practices. Both tourism policy documents analysed reflect similar linguistic patterns grounded in economic discourse and a marked absence of ecological discourses including the complete absence of the term regenerative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".