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Record W575472608

A comparison of five stakeholders' perceptions of governance under Ontario Provincial Parks' management model

2009· dissertation· en· W575472608 on OpenAlexfundaboutno aff
Windekind C. Buteau‐Duitschaever

Bibliographic record

VenueUWSpace (University of Waterloo) · 2009
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsCorporate governancePerceptionEnvironmental planningPublic administrationGeographyEnvironmental resource managementBusinessPolitical sciencePsychologyEnvironmental scienceFinance
DOInot available

Abstract

fetched live from OpenAlex

Governance is widely discussed in various government sectors or agencies such as Health Care and Education and throughout the private sector. Yet, it is only recently that reference to governance with regards to parks and protected areas has come to the for-front within various political and ecological circles. Parks and protected areas are increasingly threatened by climate change and political influences and therefore, there is a current need to assess the design and operations of protected areas so that they can be properly managed for the changes that have and will continue to occur. The current study examined how five stakeholder groups perceived 12 governance factors under Ontario Parks’ management model. Results revealed that Ontario Parks’ management model is perceived as having good levels of governance for all 12 factors by the entire population and within each of the five stakeholder groups. Differences in perception were observed primarily between the Park Staff participants when compared to the Contractor and Local Resident participants

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.652

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.065
GPT teacher head0.215
Teacher spread0.150 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations7
Published2009
Admission routes2
Has abstractyes

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