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Record W3089036784 · doi:10.1080/14615517.2020.1820848

Leveraging impact assessment for satisfactory project outcomes: benefits of early planning and participatory decision-making

2020· article· en· W3089036784 on OpenAlexaffabout
Melissa Mayhew, Jessica Perritt

Bibliographic record

VenueImpact Assessment and Project Appraisal · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsNuclear Waste Management Organization
Fundersnot available
KeywordsTimelineTransparency (behavior)Stakeholder engagementImpact assessmentProcess managementStakeholderBusinessCitizen journalismProcess (computing)Project sponsorshipProject stakeholderProject planningEnvironmental planningSocial impact assessmentEnvironmental resource managementProject managementProject charterPublic relationsComputer sciencePolitical sciencePublic administrationManagementEconomics

Abstract

fetched live from OpenAlex

This article demonstrates to proponents that adopting good practices early in the impact assessment process can help to achieve positive project outcomes. Drawing from a case study of the Adaptive Phased Management Project proposed by Canada’s Nuclear Waste Management Organization, we show ways in which impact assessment can be leveraged to achieve beneficial outcomes for interested parties through early planning that includes a commitment to transparency and participatory engagement. A considerable benefit of the approach we outline is the establishment of good relationships between stakeholders, rights-holders, and the proponent, which facilitate dialogue regarding the goals of each party and how implementing the project can help to achieve them. Examples of stakeholder, Indigenous community, and proponent collaboration during early planning for the regulatory approval process, including legislated impact assessment, of a multi-billion dollar infrastructure project with an extended timeline are provided.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.085
GPT teacher head0.453
Teacher spread0.368 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

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