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Record W2945726474

The Rebirth of Social Licence

2018· article· en· W2945726474 on OpenAlexaffabout
Kristen van de Biezenbos

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Law and Human Rights
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOpposition (politics)LegitimacyNormativePoliticsPublic opinionPublic relationsPolitical scienceIndigenousValue (mathematics)Political economyBusinessLaw and economicsSociologyLaw
DOInot available

Abstract

fetched live from OpenAlex

Canada’s energy industry and the agencies that regulate it are suffering a crisis of legitimacy. Both are battered by shifting public opinion, opposition from powerful NGOs, a troubled history with many communities and Indigenous groups, and the actions of political parties that consider opposition to oil and gas projects to be central to their platforms. In such an environment, the concept of social licence to operate, or simply social licence, seems more important than ever to the energy industry. This Article argues, however, that it is not the ability or inability to obtain social licence, as the term is currently used, that will allow the fossil fuel industry to maintain some measure of public good will and to lower municipal and provincial resistance to energy projects. That is because, while social licence has some value as a normative concept, it is functionally meaningless. Not only has the term itself been hollowed out by overuse and fluctuating definitions, but what it represents in popular discourse — a broad public acceptance or approval — is probably not achievable. For too long, the national debate over social licence has obscured the very real concerns over the local impacts of energy projects, and this has eroded the trust and support of communities. This Article proposes that the concept of social licence should be understood as descriptive only, and what should matter instead is what measures companies can take to earn that descriptor. This Article also argues that, in order to obtain acceptance from local and community groups and thus to obtain social licence, Canadian energy companies should follow the lead of companies in other jurisdictions and employ community agreements to demonstrate their commitment to responsible resource development and to earn local buy-in for projects.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.009
GPT teacher head0.212
Teacher spread0.203 · 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 designTheoretical or conceptual
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

Citations5
Published2018
Admission routes2
Has abstractyes

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