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Record W2900368046 · doi:10.18584/iipj.2018.9.3.5

Framing Indigenous Bioenergy Partnerships

2018· article· en· W2900368046 on OpenAlexafffundvenueabout
Melanie Zurba, Ryan Bullock

Bibliographic record

VenueInternational Indigenous Policy Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsUniversity of WinnipegDalhousie University
FundersSocial Sciences and Humanities Research Council of CanadaBioFuelNet Canada
KeywordsIndigenousBioenergyFraming (construction)Empirical researchEnvironmental resource managementFrame analysisCitizen journalismResource (disambiguation)BusinessEnvironmental planningPolitical scienceSociologyGeographyRenewable energyEconomicsEcologySocial scienceContent analysis

Abstract

fetched live from OpenAlex

The rapidly expanding forest bioenergy sector in Canada promises to support low carbon energy options that also support economic development and Indigenous involvement. Little empirical research has been conducted on Indigenous participation in forest bioenergy in Canada, which points to the need for a nuanced and reliable knowledge base to foster innovation in bioenergy that will contribute to community and economic development. However, before empirical research can be conducted it is important to understand the issues that influence Indigenous participation in the bioenergy sector. We therefore look to and conduct a frame analysis of allied sectors to develop insights about the policy and participatory landscape in which forest bioenergy in Canada is situated. Our analysis illustrates that identities and perspectives linked to energy and forestry can be complex and can shift depending on how business is done around such projects. Strengths in the current state of knowledge include the breadth of research regarding participatory natural resource management in Canada, particularly with regard to northern and Indigenous communities and territorial lands. Our review indicates that even the emerging bioenergy literature that exists now, when paired with that of allied sectors, can help analysts understand and make sense of energy and energy-related issues.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.052
GPT teacher head0.372
Teacher spread0.320 · 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.

Study designNot applicable
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

Citations13
Published2018
Admission routes4
Has abstractyes

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