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Record W4252129761 · doi:10.1504/ijisd.2017.083307

A comparative review of the role of markets and institutions in sustaining innovation in cleantech: a critical mass approach

2017· review· en· W4252129761 on OpenAlexaffabout
Kernaghan Webb, Randy Cruz, Philip R. Walsh

Bibliographic record

VenueInternational Journal of Innovation and Sustainable Development · 2017
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsGovernment (linguistics)Civil societyBusinessCritical mass (sociodynamics)Corporate governanceJurisdictionSustainabilityEmerging marketsPrivate sectorEconomic growthEconomicsPolitical scienceFinance

Abstract

fetched live from OpenAlex

This paper serves to examine and compare the role of markets and institutions in the adoption of clean technologies ('cleantech') in Canada, Germany and the USA. Sustainable innovation and industry growth in cleantech in a particular jurisdiction can take place when there is ongoing market pressure for cleantech and a 'critical mass' of private sector, government and academic actors, initiatives and structures that support the widespread adoption and use of cleantech. Employing Webb's (2005) sustainable governance approach as a base of analysis, it would appear that Canada lacks the density of institutions, instruments, processes and actors needed to create a critical mass to support sustainable cleantech activity in the long-term. In particular, when compared with Germany and the USA, the Canadian approach lacks key federal support and lacks the degree of private sector and civil society (academic) activity in cleantech that can be observed in those jurisdictions.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.945
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.231
GPT teacher head0.394
Teacher spread0.163 · 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.

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

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

Citations12
Published2017
Admission routes2
Has abstractyes

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