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Record W2968655777 · doi:10.32872/spb.v14i2.33399

The Role of Control Motivation in Germans’ and Poles’ Interest in History

2019· article· en· W2968655777 on OpenAlexaff
Michał Bilewicz, Anna Stefaniak, Markus Barth, Marta Witkowska, Immo Fritsche

Bibliographic record

VenueSocial Psychological Bulletin · 2019
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsCarleton University
FundersNarodowe Centrum NaukiDeutsche Forschungsgemeinschaft
KeywordsMoralityPopularityCompetence (human resources)CuriositySocial cognitive theory of moralityAgency (philosophy)Social psychologyMoral agencyPerceptionPsychologyCognitionControl (management)Moral developmentSociologySocial sciencePolitical scienceLawManagement

Abstract

fetched live from OpenAlex

Contemporary societies seem to be obsessed with history. This is reflected in the popularity of historical books, films, and reenactments. In our research, we aimed to assess the specific types of content that interest people when exploring their national histories and the psychological factors motivating such explorations. Following the two-dimensional model of social cognition that points to morality and competence as the main dimensions in individual and group perception, we distinguished interest in competence-related aspects of national history (control) from interest in historical moral actions (moral agency). Two studies performed in Poland and Germany showed that in both countries people’s interest in history is structured in a similar way, in which moral agency and control play essential roles. Additionally, in both countries people reacted to individual control threats with enhanced curiosity about the past moral agency of their nations. We discuss these results within the framework of the model of group-based control and compensatory control processes.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.087
GPT teacher head0.340
Teacher spread0.254 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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