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Record W1911603390

A critical analysis of Ontario’s resource-based tourism policy

2010· article· en· W1911603390 on OpenAlexaffabout
Nathan Bennett, Raynald Harvey Lemelin

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2010
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsLakehead UniversityUniversity of Victoria
Fundersnot available
KeywordsTourismGovernment (linguistics)Resource (disambiguation)Transparency (behavior)Context (archaeology)Political sciencePublic policyPolicy analysisBusinessPublic administrationRegional scienceEconomicsEconomic growthSociologyGeographyComputer science
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.248
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2010
Admission routes2
Has abstractyes

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