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Record W2257855901 · doi:10.31542/j.ecj.315

Breaking the Cycle: Changing Alberta in the Present to Save the Future

2015· article· en· W2257855901 on OpenAlexaffvenueabout
A. Rachelle Foss

Bibliographic record

VenueEarth Common Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsMacEwan University
Fundersnot available
KeywordsBusinessResource (disambiguation)Natural resource economicsPetroleum industryCapital (architecture)PoliticsFossil fuelAccountabilityEconomicsEconomyMarket economyEconomic policyPolitical scienceGeography

Abstract

fetched live from OpenAlex

Alberta’s resource power lies within the energy sector; in particular, the oil and gas industry. However, this same energy sector is contributing heavily to the destruction of the landscape and is contaminating the environment. This destructive pattern may seem unrelated to the province’s economy, but a closer look shows that they are, in fact, closely connected. This is largely due to a failure by both the industry and political leaders to have a vision for the economic future. Although oil and gas royalties, paid to the province for the right to use crown land for capital gain, provide considerable financing to support Alberta’s infrastructure, redirecting a large portion of those royalties back into the energy sector has contributed to the provincial budget surplus plummeting into a budget deficit. Couple this with a consistent failure to impose environmental accountability on heavily polluting energy companies and limited support for sustainable energy practices and innovations. Continuing on this path is a short sighted plan that puts both the Alberta and Canadian economies at risk as they fail to diversify and move forward with the rest of the world as it makes changes toward reducing emissions and increasingly supports ecological practices. Instead, Alberta must shift their focus away from the tradition of investing in the oil industry and, as many other regions worldwide have done, invest in renewable resources, sustainable practices, and increase support for local energy innovations to ensure the province’s energy sector, environment, and economy move towards a strong future.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.924
Threshold uncertainty score0.552

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0190.007
Scholarly communication0.0100.003
Open science0.0020.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0100.001

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 head0.024
GPT teacher head0.302
Teacher spread0.277 · 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

Citations1
Published2015
Admission routes3
Has abstractyes

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