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Record W1971082250 · doi:10.1177/00139160121973223

Altruistic, Egoistic, and Normative Effects on Curbside Recycling

2001· article· en· W1971082250 on OpenAlexaff
Gordon O. Ewing

Bibliographic record

VenueEnvironment and Behavior · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsMcGill University
Fundersnot available
KeywordsNormativeAffect (linguistics)Altruism (biology)GarbageSocial psychologyPsychologyEconomicsMicroeconomicsLawEngineeringPolitical science

Abstract

fetched live from OpenAlex

How altruistic, normative, and egoistic factors affect households’ participation in curbside recycling is shown to depend on how participation is measured. If expressed as whether a household participated, the importance of two normative factors (the expectations of household members and of friends and neighbors), an altruistic factor (that recycling helps protect the environment), and an egoistic factor (that recycling is inconvenient) appears similar. However, the altruistic factor has the greatest impact and the egoistic factor the least because of strong beliefs in curbside recycling’s environmental benefit and weak beliefs in its inconvenience. However, when measured by the proportion of different kinds of material a household recycles, the dominant influences are the expectations of other household members and inconvenience. The significance of egoistic concerns, namely, inconvenience and cost, is confirmed by negative attitudes toward user fees for garbage collection and toward drop-off depots as alternatives to curbside pickup.

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)0.010
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.242
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 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

Citations137
Published2001
Admission routes1
Has abstractyes

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