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Record W1496013422 · doi:10.20381/ruor-5415

Environmental Responsibility of a Canadian Alpine Sport Area: A Case Study

2012· dissertation· en· W1496013422 on OpenAlexaboutno aff
Nicolas Apostolis

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyEnvironmental planningPolitical scienceEnvironmental resource managementPhysical geographyEnvironmental science

Abstract

fetched live from OpenAlex

This Master’s thesis explores environmental corporate social responsibility (ECSR) in the alpine sport industry. A mixed methods case with a single alpine sport provider in Quebec was performed. The first study is a qualitative examination of how and why ECSR is employed and communicated. Results indicate ECSR is strategically motivated, and as such, the focal organization runs the risk of using several greenwashing techniques in communications that could jeopardize gaining competitive advantage. The second study quantitatively investigates alpine sport consumers’ environmental expectations, engagement with environmental products, and perceptions of the focal organization’s environmental reputation. The results show the focal organization’s customers do indeed have environmental expectations, but believe the focal organization’s environmental reputation remains neutral. The thesis supports arguments of corporate social responsibility (CSR) being strategically motivated and other findings of skiers having conflicting environmental values. Lastly, the thesis provides insight regarding greenwashing, a phenomenon that remains unexplored in sport management.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.278

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0170.003
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.284
Teacher spread0.267 · 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 designObservational
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

Citations0
Published2012
Admission routes1
Has abstractyes

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