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Record W7048047850

Industrial Strategies for Green Jobs: Opportunities and Obstacles in the Ontario Case

2022· report· en· W7048047850 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2022
Typereport
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsnot available
Fundersnot available
KeywordsProcurementGovernment (linguistics)SubsidyInvestment (military)DilemmaGovernment procurementOpenness to experienceState (computer science)Industrial policy
DOInot available

Abstract

fetched live from OpenAlex

Doing something about the environmental crisis without harming the economy and jobs has been a dilemma for governments for many years. This paper explores the potential and opportunities conferred by green jobs economic strategies using the example of Ontario's Green Energy policy. This case also highlights the obstacles to achieving that positive sum result posed by international economic agreements. Trade agreements like NAFTA and the WTO, however, may have an impact on state capacity to enact and implement industrial policies, since green economic strategies can be seen as a particular variant of an industrial strategy. The domestic content provisions in Ontario's Green Energy Act, and alleged subsidization through the FIT have already triggered trade complaints and an action by Japan. Government procurement is a central plank in the defence of Ontario's policy, though one that is threatened by possibly enhanced procurement openness that Canada is negotiating, with provinces at the table, in new economic agreements such as CETA. Outcomes are uncertain but as this case study shows trade and investment agreements do pose a challenge to green industrial policies especially if government procurement protections are sacrificed or substantially weakened.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.001
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 head0.100
GPT teacher head0.215
Teacher spread0.115 · 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 designNot applicable
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

Citations1
Published2022
Admission routes1
Has abstractyes

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