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Record W2225562182 · doi:10.1177/1086026615623057

Lack of Stakeholder Influence on Pollution Prevention

2016· article· en· W2225562182 on OpenAlexaff
Asadul Hoque, Amelia Clarke, Lei Huang

Bibliographic record

VenueOrganization & Environment · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStakeholderCivil societyContext (archaeology)Government (linguistics)Developing countryBusinessPublic relationsPolitical scienceStakeholder analysisEnvironmental planningEnvironmental resource managementEconomic growthPoliticsGeographyEconomics

Abstract

fetched live from OpenAlex

In a developing country context, this study explores environmental awareness, stakeholder influence strategies, and pollution prevention roles among 11 local, civil society groups (e.g., environmental nongovernmental organizations [NGOs] is one grouping; media and press is another grouping). A theoretical framework that builds on the social movement literature and is more inclusive of a developing country context is offered. In essence, awareness-raising is also considered a stakeholder influence strategy. Based on surveys conducted in Chittagong, Bangladesh, the results of this empirical study show that 10 of the 11 groups were environmentally aware; however, only the environmental NGOs were willing to influence the other groups. The environmental NGOs were actively raising awareness, but they were not directly influencing firms or the federal government on pollution prevention. These findings challenge the generalization of current stakeholder influence theory to a developing country context and raise concerns about the capacity of local civil society to encourage pollution prevention.

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)0.004
metaresearch head score (Gemma)0.012
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 score0.006
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0000.004
Research integrity0.0010.001
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 head0.024
GPT teacher head0.213
Teacher spread0.190 · 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

Citations23
Published2016
Admission routes1
Has abstractyes

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