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Record W2947203848 · doi:10.1080/14615517.2019.1601432

Introducing SEA effectiveness

2019· article· en· W2947203848 on OpenAlex
Riki Thérivel, Ainhoa González

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueImpact Assessment and Project Appraisal · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceComputer science

Abstract

fetched live from OpenAlex

The idea for this special issue – IAPA 2019, issues 3 and 4–startedwitharesearchprojectonSEAeffectivenessin Ireland commissioned by the Irish Environmental Protection Agency (EPA 2018). As part of the project, we were to examine and learn from SEA effectiveness in other countries. At Thomas Fischer’s prompting, this evolved into inviting people to write articles about SEA effectiveness in their country. We wrote to about 20 people, expecting a few responses. Instead, we got more than a dozen. This confirms that SEA effectiveness is an important and timely topic – as does the ongoing European Commission ‘REFIT’ of the SEA Directive (EC 2018). This special issue not only provides insights into the performance of SEA across Europe and globally, but can also inform the EU REFIT. We are delighted to present, in this issue, articles about SEA effectiveness in Austria, Brazil, Canada, the Czech Republic, England, Estonia, Germany, Ireland, Poland, Portugal, Romania, Scotland, Spain, Slovenia and Thailand. Most of the contributors have carried out primary research in the form of questionnaires or discussion groups. This has allowed an analysis of SEA’s performance on the ground, by looking at actual changes to the plan and plan implementation, as well as tapping into authors’ personal knowledge. In turn, this enables a more comprehensive examination of SEA effectiveness, rather than simply whether SEA reports cover specific aspects and topics, which has been the focus of most previous effectiveness studies. This introductory editorial gives brief background information about the dimensions of effectiveness that we asked the article authors to address (i.e. contextual, pluralist, substantive, normative, knowledge and learning, and transactive); the procedural dimension which we explicitly asked them to not address; and some of the issues emerging from the articles.

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 categoriesInsufficient 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 score0.033
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.012
GPT teacher head0.370
Teacher spread0.359 · 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