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Record W6996722315

Shared geothermal energy projects in Montreal : The importance of pre-existing collective action spaces

2022· other· en· W6996722315 on OpenAlexaboutno aff

Bibliographic record

VenueEspaceINRS (National Institute for Scientific Research (Canada)) · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCollective actionContext (archaeology)Renewable energyAction (physics)Energy (signal processing)Energy transitionRelation (database)HydroelectricityEnergy policy
DOInot available

Abstract

fetched live from OpenAlex

Interest and support for decentralized, shared, community-managed, renewable energy systems has been growing in different countries. In Montreal, local energy projects have emerged despite the fact that the Quebec energy portrait is largely dominated by hydroelectric power and offers a particular context for energy projects, especially when it comes to decentralized renewable energy. Through a series of interviews with initiators of shared geothermal projects, we study one factor for their emergence which is still understudied in the literature – the pre-existing collective action spaces contributing to the development of novel energy initiatives. We begin by presenting the Quebec context for energy provision and the limitations of local authorities. We then follow with a discussion of the energy community and the role of existing collective action spaces. Two initiatives are finally presented and compared to better understand their trajectories in relation to the type of collective action spaces on which they were built. Our results show that, while pre-existing structures and spaces of collective action have helped with the emergence of these initiatives, they have also affected their trajectories and the challenges they face.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.005
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.075
GPT teacher head0.344
Teacher spread0.269 · 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 designQualitative
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

Citations0
Published2022
Admission routes1
Has abstractyes

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