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Community Involvement In The Hybrid Organization: A Study Of Community Forest Enterprises in Canada

2022· article· en· W4283837675 on OpenAlexaffabout
Meike Siegner, Robert Kozak

Bibliographic record

VenueAcademy of Management Proceedings · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEmpowermentContext (archaeology)SustainabilityPublic relationsPluralIndigenousSociologyLocal communityCitizen journalismBusinessPolitical scienceEnvironmental resource managementEcologyEconomicsGeography

Abstract

fetched live from OpenAlex

Effectively involving local communities in decision-making is an important facet of all organizations which aim to be socially responsible, but it is particularly relevant for social enterprises (SEs) which espouse community empowerment as one of their central goals. The extant literature, however, provides little in the way of guidance for SEs to ensure local participation in strategic and operational matters. This qualitative study illustrates mechanisms by which SEs can involve local people in organizational decisions in the context of six Canadian community forest enterprises (CFEs). CFEs have evolved as a result of the movement to decentralize decision-making with respect to the sustainable utilization of natural resources. This article provides insights on the importance of managerial capabilities in navigating participatory activities in the SE. We propose an enriched SE tension management framework, that includes a key dimension that has yet to receive much attention: the distinction between a focus on ‘outcomes’ versus a focus on ‘process’ in the attainment of plural goals. As such, the article makes important contributions to unpacking the empowerment element underlying organizational activities that aim to provide marginalized rural and Indigenous communities with roles in shaping local paths towards greater sustainability.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.001
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 head0.023
GPT teacher head0.236
Teacher spread0.213 · 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 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
Published2022
Admission routes2
Has abstractyes

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