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Record W4230233558 · doi:10.46863/ecocene.2020.7

Alberta and the Global Commons: A Climate Change Tragedy

2020· article· en· W4230233558 on OpenAlexaffabout
Robert Boschman

Bibliographic record

VenueEcocene Cappadocia Journal of Environmental Humanities Cappadocia University · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsMount Royal University
Fundersnot available
KeywordsTragedy of the commonsCommonsClimate changeTragedy (event)Global commonsPolitical scienceEnvironmental ethicsEnvironmental resource managementGeographyEnvironmental scienceSociologyEcologyPhilosophySocial scienceBiologyLaw

Abstract

fetched live from OpenAlex

The Canadian province of Alberta contains the third-largest proven reserve of oil on earth, yet the disconnect between politics and the sciences has never been more severe or as consequential. A right-wing party given to authoritarianism has recently been elected in Alberta that is taking actions to ensure the continued extraction and transport of bitumen from the tar sands in the north. Despite the three recent warnings by scientists (beginning in 2017) concerning global climate change tipping points—and specifically that fossil fuel reserves must remain in the ground—the government of Jason Kenney continues Alberta’s carbon-intensive extractive activities while waging destructive political engagement with Canada and the world. This essay documents Alberta in terms of the model provided by classical tragedy and highlights three acts: 1. The Great Flood of 2013; 2. The Great Fire of 2016; and 3. The Orphan Wells of 2020. In the tragic denouement currently underway here, Alberta’s reckless actions impact the global commons and affect all earthlings.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.066
Threshold uncertainty score0.348

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.009
Scholarly communication0.0070.002
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.060
GPT teacher head0.182
Teacher spread0.122 · 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 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

Citations0
Published2020
Admission routes2
Has abstractyes

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