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Record W2512331379 · doi:10.1142/s1464333216500204

Aboriginal Participation in Canadian Environmental Assessment: Gap Analysis and Directions for Scholarly Research

2016· article· en· W2512331379 on OpenAlexaffabout
Aniekan Udofia, Bram Noble, Greg Poelzer

Bibliographic record

VenueJournal of Environmental Assessment Policy and Management · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTimelineScope (computer science)Political scienceProcess (computing)Public relationsTest (biology)SociologyEnvironmental planningGeographyComputer science

Abstract

fetched live from OpenAlex

There has emerged in recent years an increased industry and regulatory demand for the streamlining of environmental assessment (EA), and at the same time, persistent expectations by Aboriginal communities for more effective and meaningful engagement in development decisions. This paper examines the extent to which scholarly research has contributed to solutions for meaningful Aboriginal participation amidst demands for more efficient and shorter timelines for participation and decision-making. Three research priorities are identified from our assessment of peer-reviewed EA scholarly research: the need for empirical-based research assessing the impacts of streamlining on participation and the impacts of meaningful Aboriginal participation on EA efficiencies; the need for better defined scope of issues that should be addressed inside the EA process versus those that are best addressed external to EA; and the need to develop and test alternative mechanisms for Aboriginal participation at the regional and strategic levels, and their contributions to regulatory-based EA decisions.

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 score0.039
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.028
GPT teacher head0.407
Teacher spread0.379 · 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

Citations6
Published2016
Admission routes2
Has abstractyes

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