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Record W2477393090 · doi:10.1002/9781119162124.ch6

Who Cares? Measuring Environmental Attitudes

2016· other· en· W2477393090 on OpenAlexaff
Amanda McIntyre, Taciano L. Milfont

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsScale (ratio)Reliability (semiconductor)Measure (data warehouse)Environmental psychologyPsychologyField (mathematics)Curse of dimensionalityApplied psychologyData scienceSocial psychologyComputer scienceGeographyMathematicsData miningArtificial intelligenceCartography

Abstract

fetched live from OpenAlex

This chapter discusses the measurement of environmental attitudes within the field of environmental psychology. It provides readers with a definition of environmental attitudes, along with a discussion of why it is important to measure them. The chapter proceeds with a list of the many well-established measures of environmental attitudes including a brief description of each measure. Measures for adults and children/adolescents are provided, as well as measures that focus on attitudes toward natural and/or built environments. Some guidelines are also presented for how to choose and use an environmental attitudes measure, including a discussion of reliability and validity, dimensionality, scale modification, scale length, and response biases. Current trends in the area are also discussed, including the use of explicit versus implicit measures, theory building and meta-analyses with regards to environmental attitudes, in hopes to guide future research on the measurement of environmental attitudes.

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)0.004
metaresearch head score (Gemma)0.021
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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

Citations50
Published2016
Admission routes1
Has abstractyes

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