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Record W3200262584

Federalism, the Environment and the Rule of Law

2019· other· en· W3200262584 on OpenAlexaboutno aff
Federica Cittadino

Bibliographic record

VenueView · 2019
Typeother
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsnot available
Fundersnot available
KeywordsFederalismRule of lawLawPolitical scienceLaw and economicsEconomicsPolitics
DOInot available

Abstract

fetched live from OpenAlex

The group work is designed both to discuss in which ways federalism and federal theory support power-sharing in environmental matters and to understand how the multilevel governance of the environment works in the practice of selected constitutional systems in selected sectors. Constitutional arrangements regarding the division of legislative powers between the federal/central state and its subnational entities may change over time. In this respect, scholars discuss the rationale for splitting environmental powers between the centre and the periphery. The workshop will explore those issues with a view to understanding the relevance of abstract criteria in the division of environmental powers. To this end, in the first part of the workshop, the instructor will present a contribution by Mostert (2015) that discusses the different principles for allocating responsibilities in environmental matters among different levels of government. In the second part, discussion will focus on the practice of federal States (Argentina, Australia, Brazil, Canada, India, Russia, and United States) to understand how the multilevel governance of the environment works in practice. To this end, participants will present the contributions assigned to them by the instructor. The eight participants are divided into four groups made of two persons each (see below). The first member of the group will present the content of the article, while the second one will act as a discussant, thus highlighting main problematic points, proposing counter-arguments to the theses argued in the contribution presented by the other presenter, and raising questions to be discussed with the rest of the audience.

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)0.008
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.037
Scholarly communication0.0100.007
Open science0.0010.004
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.269
Teacher spread0.254 · 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
Published2019
Admission routes1
Has abstractyes

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