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Record W1993769191 · doi:10.1108/17582951111136595

Actualizing environmental sustainability at Vancouver 2010 venues

2011· article· en· W1993769191 on OpenAlexaffabout
Ian F. Ponsford

Bibliographic record

VenueInternational Journal of Event and Festival Management · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSustainabilityRubricContext (archaeology)Event (particle physics)OriginalityBusinessPublic relationsPlan (archaeology)Process managementKnowledge managementSociologyPolitical scienceComputer scienceCreativity

Abstract

fetched live from OpenAlex

Purpose Philosophical espousal of the sustainability rubric is becoming widespread in the event industry. A business systems‐oriented literature has emerged that helps event organizers plan for and measure success of more environmentally responsible or sustainable events. While top‐down approaches to sustainability are increasingly well established, the practical application of these programs in the unique event planning and management context is less well understood. This paper aims to build understanding of operations‐level opportunities and constraints that may be faced by environmental practitioners in event organizations. Design/methodology/approach Semi‐structured interviews were conducted with members of the Vancouver 2010 Organizing Committee's (VANOC) Environmental Management Team (EMT), a group of environmental professionals embedded in VANOC's venue infrastructure business unit. Findings Several organizational strategies are described but intra‐organizational relationships are found to be the medium by which the environmental sustainability concept is actualized at venues. Originality/value Although the EMT operated within the standardized event delivery model established by the International Olympic Committee, it is hoped the Vancouver 2010 experience will be useful for other event organizers instituting and delivering environmental programs.

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 score0.600
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.302
Teacher spread0.275 · 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

Citations21
Published2011
Admission routes2
Has abstractyes

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