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Record W2564691520 · doi:10.5539/ijps.v9n1p62

Be Empathic! The Influence of Empathy on Attitude Formation in the German Refugee Debate

2016· article· en· W2564691520 on OpenAlexvenueno aff
Julia F. Weber, Marc‐André Reinhard

Bibliographic record

VenueInternational Journal of Psychological Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsEmpathyPsychologyRefugeeGermanSocial psychologyFeelingPersonal distressEmpathic concernMoodDevelopmental psychologyPerspective-takingPolitical science

Abstract

fetched live from OpenAlex

The study was conducted to investigate the elaboration and memorisation of the emotionally charged refugee debate with reference to the possible influences of empathic feelings, political attitude and mood. It was hypothesised that empathy towards refugees correlates positively with advanced elaboration and memorisation of the refugee debate. Participants’ personal empathy was assessed through a self-report questionnaire which differentiates between four distinct dimensions of empathy. Afterwards, participants listened to an interview with a politician from a German populist party about refugee policies concluding with a test about the content. As expected, the results revealed a positive correlation between the empathy dimensions’ Fantasy and Personal Distress and the correct answers of the open-ended question test. The Fantasy dimension of empathy was significantly correlated with the elaboration and memorisation of the extreme and populist positions in the refugee debate. Important practical implications as well as limitations of the study were discussed.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.103
GPT teacher head0.470
Teacher spread0.367 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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