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Institutional Frame Switching: How Institutional Logics Shape Individual Action

2016· book-chapter· en· W2562726392 on OpenAlex
Vern Glaser, Nathanael J. Fast, Derek Harmon, Sandy E. Green

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSituational ethicsPerspective (graphical)Associative propertyInstitutional theoryAction (physics)MacroFrame (networking)Schema (genetic algorithms)PsychologySocial psychologySociologyComputer scienceArtificial intelligenceMathematicsPure mathematics

Abstract

fetched live from OpenAlex

Abstract Although scholars increasingly use institutional logics to explain macro-level phenomena, we still know little about the micro-level psychological mechanisms by which institutional logics shape individual action. In this paper, we propose that individuals internalize institutional logics as an associative network of schemas that shapes individual actions through a process we call institutional frame switching. Specifically, we conduct two novel experiments that demonstrate how one particularly important schema associated with institutional logics – the implicit theory – can drive individual action. This work further develops the psychological underpinnings of the institutional logics perspective by connecting macro-level cultural understandings with micro-level situational behavior.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.857
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0380.003

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 head0.244
GPT teacher head0.364
Teacher spread0.120 · 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

Quick stats

Citations61
Published2016
Admission routes1
Has abstractyes

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