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Record W7051761558

Panel Reviews Under the Proposed Federal Impact Assessment Act (IAA)

2018· article· en· W7051761558 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2018
Typearticle
Languageen
FieldEngineering
TopicParticle Accelerators and Free-Electron Lasers
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)Context (archaeology)TimelineProcess (computing)Impact assessmentDecision process
DOInot available

Abstract

fetched live from OpenAlex

Introduction\nEnvironmental assessments in Canada have gone through many changes over the past 40 years. The federal process has evolved from the 1973 EARP process to the 1984 EARP Guidelines Order, the 1992, 2003, 2010, and 2012 versions of CEAA, and now the proposed IAA under Bill C-69. Through these various transitions, there have been changes to the triggering mechanism, the process options, scope and decision making (among others). A constant throughout has been that Review Panels have served as the high-water mark of assessments under the federal process. Of course, Review Panels have not been immune to change during this 40-year evolution of federal EA, and they have been affected by other changes to federal EA. In this post, we consider the proposed changes in Bill C-69 as they relate to the process for Review Panels. We consider these changes in the broader context of the transition that Review Panels have undergone prior to the introduction of Bill C-69. We particularly note the following in this regard: There has been a trend to reduce the Panel’s role in determining the scope of assessments, with the Minister gradually taking over the role of making scope determinations (in the form of the Terms of Reference for the Panel and the EIS Guidelines issued to the proponent). Both are now finalized before Panel’s are appointed. There has been a trend toward imposing legislated timelines on Review Panel processes. There has been a trend away from asking Panels to offer overall recommendations and conclusions about a proposed project. There has been a trend toward Joint Review Panels that include other interested jurisdictions as well as regulators. There has been a trend toward fewer Review Panels under the federal EA process, using substitution and other process options to reduce the number of Review Panels carried out.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.057
metaresearch head score (Gemma)0.078
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.250
Threshold uncertainty score0.497

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0570.078
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0080.003
Scholarly communication0.0130.004
Open science0.0050.003
Research integrity0.0090.006
Insufficient payload (model declined to judge)0.0180.013

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 head0.032
GPT teacher head0.293
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2018
Admission routes1
Has abstractyes

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