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Record W3198092386 · doi:10.1177/10860266211042659

Reconciling Institutional Logics Within First Nations Forestry-Based Social Enterprises

2021· article· en· W3198092386 on OpenAlexaffabout
Anthony W. Persaud, Harry W. Nelson, Terre Satterfield

Bibliographic record

VenueOrganization & Environment · 2021
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIndigenousPolitical scienceSociologyThrough-the-lens meteringEconomic systemEconomicsLens (geology)Ecology

Abstract

fetched live from OpenAlex

The institutional frameworks that Indigenous groups put in place to govern economic processes within their communities are critical to the advancement of their diverse cultural-ecological, social, and economic development goals. Through the lens of institutional logics, this article examines the ways in which First Nations community sawmill enterprises in British Columbia, Canada, navigate the sectoral demands brought by a productivist paradigm of forestry. We find that First Nations community sawmill enterprises represent spaces of both logical tension and innovation where conflicts that arise between dominant “commercial” logics and culturally legitimate “Indigenous” logics can be reconciled. Through this analysis, this article offers an empirical example of the emergence of Indigenous institutional frameworks, as well as a contribution to the growing body of literature that addresses the ways in which hybrid organizations can and do navigate and overcome conflicting institutional logics.

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)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.380

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0120.043
Scholarly communication0.0140.004
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.170
Teacher spread0.161 · 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 designQualitative
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

Citations11
Published2021
Admission routes2
Has abstractyes

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