A critical analysis of Ontario’s resource-based tourism policy
Bibliographic record
Abstract
Conflicts between resource-based industries and resource-based tourism are commonplace, complex, and long lived. The Resource-Based Tourism Policy (Government of Ontario, 1997) was one of a number of documents produced by the Government of Ontario in response to such conflicts in Northern Ontario, Canada. Yet in the 13 years since the policy was produced, there has been no research to examine either the impact or effectiveness of this document in achieving its stated goal: “to promote and encourage the development of the Ontario resource-based tourism industry in both an ecologically and economically sustainable manner” (Government of Ontario, 1997, p. 1). This article reviews the context within which the policy operates, summarizes the policy document, and questions both the impact and effectiveness of the Resource-Based Tourism Policy based on five critiques: (a) the level of transparency, collaboration, and representation in the policy’s development; (b) the unity of the policy direction and actions; (c) the incorporation of science into proposed policy solutions; (d) the adaptability of the policy to changing industry and contextual trends; and (e) the completeness of the policy’s implementation. In conclusion, we suggest that it is time to revisit, reexamine, adapt, and update this policy document in consideration of current trends in the industry and contextual factors.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.034 | 0.013 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| 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".