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Record W3131246766 · doi:10.11575/prism/35971

Power Of The Individual: Saving Energy Through Conscious Consumption

2016· article· en· W3131246766 on OpenAlexaboutno aff
Lidia Sorial

Bibliographic record

VenuePRISM (University of Calgary) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPsychology of Social Influence
Canadian institutionsnot available
Fundersnot available
KeywordsPower (physics)Consumption (sociology)Energy consumptionEconomicsBusinessSociologyEngineeringElectrical engineeringSocial sciencePhysics

Abstract

fetched live from OpenAlex

The purpose of this study is to capitalize on an opportunity to become more sustainable. The points in question are: How much energy savings can be achieved through conscious consumption alone? What changes are people willing to make at their own discretion? Are there any correlations between certain demographics and energy use? Experimental results from 10 households showed that the minimum savings achieved over one month are equal to a potential annual savings of 250,000 GJ of natural gas, 43 million kWh of electricity, and 5 million m3 of water across Calgary. Water-related changes were the most commonly applied by participants. Successful households had more members, more kids, slightly higher education levels, more awareness in reality, and less awareness by perception compared to their less successful peers and the applicable statistics. Overall, energy-saving tips were deemed an effective mechanism by which to notably reduce energy consumption in an urban setting.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.264
Teacher spread0.235 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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