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Record W1988312362 · doi:10.1002/smj.439

Stakeholder influences on sustainability practices in the Canadian forest products industry

2004· article· en· W1988312362 on OpenAlexaffabout
Sanjay Sharma, Irene Henriques

Bibliographic record

VenueStrategic Management Journal · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsYork UniversityWilfrid Laurier University
Fundersnot available
KeywordsSustainabilityBusinessStakeholderForest industryIndustrial organizationEnvironmental resource managementMarketingEconomicsManagementEcologyForestry

Abstract

fetched live from OpenAlex

Abstract We examined how managers' perceptions of different types of stakeholder influences in the Canadian forestry industry affect the types of sustainability practices that their firms adopt. Both influences involving withholding of resources by social and ecological stakeholders and those involving directed usage of resources from economic stakeholders were found to drive such practices. We found that the industry and its stakeholders have moved beyond a focus on early stages of sustainability performance such as pollution control and eco‐efficiency. However, more advanced practices, such as those involving the redefinition of business and industrial ecosystems where firms locate in a region so that they can exchange and utilize wastes generated by other firms, are in their infancy. Stakeholders and firms in the industry are focused on the intermediate sustainability phases involving recirculation of materials and redesign of processes including sustainable harvesting of lumber. Copyright © 2004 John Wiley & Sons, Ltd.

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)0.007
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 score0.115
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.061
GPT teacher head0.277
Teacher spread0.216 · 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

Citations1,296
Published2004
Admission routes2
Has abstractyes

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