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Record W2020655902 · doi:10.1086/658469

When Your World Must Be Defended: Choosing Products to Justify the System

2011· article· en· W2020655902 on OpenAlex
Keisha M. Cutright, Eugenia Wu, Jillian C. Banfield, Aaron C. Kay, Gavan J. Fitzsimons

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

VenueJournal of Consumer Research · 2011
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLibrary scienceSociologyComputer science

Abstract

fetched live from OpenAlex

Consumers are often strongly motivated to view themselves as part of a legitimate and fair external system. Our research focuses on how individuals adopt distinct ways of defending their system when it is threatened and, in particular, how this is revealed in their consumption choices. We find that although individuals differ in how confident they are in the legitimacy of their system, they do not differ in their motivation to defend the system when it is threatened. Instead, they simply adopt different methods of defense. Specifically, when an important system is (verbally) attacked, individuals who are the least confident in the legitimacy of the system seek and appreciate consumption choices that allow them to indirectly and subtly defend the system. Conversely, individuals who are highly confident in the system reject indirect opportunities of defense and seek consumption choices that allow them to defend the system in direct and explicit ways.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.624
GPT teacher head0.419
Teacher spread0.205 · 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