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Record W2045282819 · doi:10.1002/bse.604

Firm strategy and the Canadian Voluntary Climate Challenge and Registry (VCR)

2007· article· en· W2045282819 on OpenAlexaboutno aff
Keith Brouhle, Donna Ramirez Harrington

Bibliographic record

VenueBusiness Strategy and the Environment · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTurnoverBusinessQuality (philosophy)Action (physics)Greenhouse gasPerceptionClimate changeVoluntary actionVoluntary disclosureMarketingEconomicsAccountingPsychologyManagement

Abstract

fetched live from OpenAlex

Abstract The Canadian VCR is a climate change mitigation program that relies on firms' desire to signal environmental responsibility to external stakeholders through voluntary information disclosure. We analyze indicators of strategic behavior through three measures of engagement with the VCR program (annual participation behavior, quality of action plans and repeat participation), and test for differences in these measures among firms subjected to different regulatory climates that arise over time, across provinces and across economic sectors. Our findings suggest an increased perception of a regulatory threat in later years, as evidenced by an increase in participation rates, higher quality of action plans and higher rates of repeat participation. We also find higher levels of engagement with the VCR program in provinces with large petroleum (Alberta) and manufacturing (Ontario) industries and that have established provincial level greenhouse gas reporting mechanisms, and in certain sectors such as petroleum, electric utilities and to some extent services. Copyright © 2007 John Wiley & Sons, Ltd and ERP Environment.

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)0.011
metaresearch head score (Gemma)0.047
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.968
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0040.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.201
Teacher spread0.183 · 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

Citations44
Published2007
Admission routes1
Has abstractyes

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