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Record W7024633136

Social Impact Assessment of Alternative Energy Production in Alberta

2017· article· en· W7024633136 on OpenAlexaboutno aff

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionGestational periodFusible alloyArticular cartilage damageHyporeflexiaProteogenomics
DOInot available

Abstract

fetched live from OpenAlex

Energy production is a large part of Alberta's economy, with direct impacts on rural landscapes and host communities across the province. This report provides an analysis of social impacts from alternative energy production, with a focus on biofuels, solar, geothermal and wind power technologies. Using comparative case study methods, research includes key informant interviews, published literature and other sources of available data from project proponents and rural community sources. The report utilizes a common assessment framework that is based in community capacity and adaptation. Results point to the ways in which alternative energy can enhance community capacities through contributions to economic infrastructure, alternative sources of employment, human capital development and social mobilization. Research also notes the importance of public participation as a key aspect of project development, especially in the early planning and implementation phase of the project. Finally, research indicates that although renewable energy has great potential in Alberta, conventional sources of energy production are perceived to be adequate and cost effective in many ways, resulting in a reluctance to invest over the short term in somewhat expensive and controversial technologies.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.374

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.327
Teacher spread0.298 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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