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Record W3041767306 · doi:10.24124/c677/20201743

Carbon Capital’s Political Reach: A Network Analysis Of Federal Lobbying By The Fossil Fuel Industry From Harper To Trudeau

2020· article· en· W3041767306 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Political Science Review · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPoliticsState (computer science)Capital (architecture)Fossil fuelPower (physics)Control (management)EconomicsPolitical economyPolitical scienceLawManagementEngineering

Abstract

fetched live from OpenAlex

This paper provides a network analysis of federal lobbying in Canada by the fossil fuel industry over a seven-year period from January 4, 2011 to January 30, 2018, enabling a comparative examination of lobbying under the Harper Conservatives and the Trudeau Liberals. The network we uncover amounts to ‘small world’ of intense interaction among relatively few lobbyists/firms that control much of this economic sector and the designated public office holders in select centres of state power, who are their targets. In comparing lobbying across the Harper and Trudeau administrations, we find a pattern of continuity-in-change: under Trudeau, the bulk of lobbying has been carried out by the same large firms as under Harper, while the lobbying network has become more focused on fewer state agencies. We argue that the strategic, organized, and sustained lobbying efforts of the fossil fuel sector help to explain the close coupling of federal policy to the needs of carbon extractive corporations.

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.

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)0.005
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: Empirical · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.005
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.322
Teacher spread0.282 · 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