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Record W2754734593 · doi:10.1016/bs.aesp.2017.08.001

Attitudes Towards Science

2017· book-chapter· en· W2754734593 on OpenAlexaff
Bastiaan T. Rutjens, Steven J. Heine, Robbie M. Sutton, Frenk van Harreveld

Bibliographic record

VenueAdvances in experimental social psychology · 2017
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIdeologyMoralityFaithEpistemologySociologyPoliticsPsychological sciencePolitical scienceSocial sciencePsychologyEngineering ethicsSocial psychologyLawPhilosophyEngineering

Abstract

fetched live from OpenAlex

As science continues to progress, attitudes toward science seem to become ever more polarized. Whereas some put their faith in science, others routinely reject and dismiss scientific evidence. This chapter provides an integration of recent research on how people evaluate science. We organize our chapter along three research topics that are most relevant to this goal: ideology, motivation, and morality. We review the relations of political and religious ideologies to science attitudes, discuss the psychological functions and motivational underpinnings of belief in science, and describe work looking at the role of morality when evaluating science and scientists. In the final part of the chapter, we apply what we know about science evaluations to the current crisis of faith in science and the open science movement. Here, we also take into account the increased accessibility and popularization of science and the (perceived) relations between science and industry.

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.999
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.002
Open science0.0000.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0130.003

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 head0.064
GPT teacher head0.489
Teacher spread0.426 · 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.

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

Citations156
Published2017
Admission routes1
Has abstractyes

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