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Record W2091156840 · doi:10.1080/14615517.2014.992673

Targeting the transitions: applying stage-gate <sup>®</sup> thinking in strategic environmental assessment

2015· article· en· W2091156840 on OpenAlexaff
Christopher R. Whynacht, Peter N. Duinker

Bibliographic record

VenueImpact Assessment and Project Appraisal · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsDalhousie University
Fundersnot available
KeywordsRigourStrategic environmental assessmentProcess (computing)MacroProcess managementComputer scienceProduct (mathematics)Stage (stratigraphy)New product developmentEnvironmental impact assessmentPolitical scienceBusinessManagementEpistemologyGeologyEconomics

Abstract

fetched live from OpenAlex

Strategic environmental assessment (SEA) practitioners have a tendency to emphasize assessment phases over the linkages between the phases. Explicitly addressing the transitions between stages could add rigour to SEA processes and make them more structurally robust. There is a considerable body of research on project management practices used by corporations during new product development (NPD). The stage-gate® model is presented as an example of a successful and innovative NPD approach that is frequently used by various industries, and which addresses the links between assessment phases. Stage-gate theory treats connections between project stages as gates which must be passed. The philosophy behind the stage-gate model is explored as a macro-process that could support current practices in SEA and other types of impact assessment. The stages and gates designed for a proposed Nova Scotia SEA framework are presented to show how stage-gate thinking can be adapted for use in SEA processes. The paper concludes that SEA processes could become more efficient and effective by integrating a philosophy of gated assessments.

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 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 score0.366
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.372
Teacher spread0.330 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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