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Record W2606350886 · doi:10.1142/s1464333217500041

New Process, Same Doubts: Participants’ Perceptions of Strategic Environmental Assessment in Western Newfoundland

2017· article· en· W2606350886 on OpenAlexafffundabout
Morgan Vespa, A. John Sinclair, Morrissa Boerchers, Robert Gibson

Bibliographic record

VenueJournal of Environmental Assessment Policy and Management · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsUniversity of WaterlooUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDistrustCredibilityProcess (computing)Strategic environmental assessmentFeelingPerceptionPublic relationsOrder (exchange)Environmental planningPolitical sciencePsychologyEnvironmental resource managementEnvironmental impact assessmentBusinessSocial psychologyGeographyEnvironmental scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

In order to learn about public participation in Strategic Environmental Assessment (SEA) and identify opportunities for improvement of SEA, this research examined the Western Newfoundland Offshore Area SEA case in Canada. Through analysis of observation and interview data, findings reveal that participants involved recognized the shortcomings of the SEA process implemented despite being SEA neophytes. Data also showed that participants were aware that the basics of meaningful public consultation were not met. Participants recognized the need for alternative approaches and possibly an alternative name for the process in which they participated. They also indicated their dissatisfaction with the SEA, and related participation activities, noting they were left with feelings of distrust in the process and the sense that decisions were foregone. These findings undermine the credibility of SEA and erode its potential as a promising tool for enhancing policy, plans, and programs and eventually project 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.035
GPT teacher head0.356
Teacher spread0.321 · 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 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

Citations10
Published2017
Admission routes3
Has abstractyes

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