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Record W4248191453 · doi:10.1142/s146433320000014x

STRATEGIC ENVIRONMENTAL ASSESSMENT: WHAT IS IT? & WHAT MAKES IT STRATEGIC?

2000· article· en· W4248191453 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Environmental Assessment Policy and Management · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsMemorial University of Newfoundland
FundersU.S. Department of Energy
KeywordsStrategic environmental assessmentStrategic planningManagement scienceEnvironmental impact assessmentProcess managementBusinessEnvironmental planningEnvironmental resource managementRisk analysis (engineering)Political scienceEngineeringEconomicsEnvironmental scienceMarketing

Abstract

fetched live from OpenAlex

This paper highlights perhaps one of the most fundamental issues constraining strategic environmental assessment (SEA) practice — its definition. Current reviews fail to explain why certain assessments are referred to as strategic while others are not. Furthermore, there appears to be very little attention given to the basic characteristics of strategy in the environmental assessment of proposed or existing policies, plans and programmes. This paper attempts to identify the characteristics of SEA that make it strategic and therefore different from other forms of impact assessment. A review of selected case studies is undertaken with the purpose of identifying those assessments that actually conform to the characteristics of a "strategic assessment". It is argued here that if SEA methodology and practice is to advance, then a common understanding of its definition and characteristics must first be achieved.

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.

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), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.006
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0360.001

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 head0.320
Teacher spread0.292 · 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