MétaCan
Menu
Back to cohort
Record W2071106124 · doi:10.1080/00049530412331312824

International images and mass media: the effects of media coverage on Canadians' perceptions of ethnic and race relations in Australia

2003· article· en· W2071106124 on OpenAlexaffabout
Julie M. Duck, Richard N. Lalonde, Deena Weiss

Bibliographic record

VenueAustralian Journal of Psychology · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsYork University
Fundersnot available
KeywordsEmotiveEthnic groupMass mediaPsychologySocial psychologyPerceptionImmigrationIndigenousPolitical scienceSociology

Abstract

fetched live from OpenAlex

Recently, there has been much speculation about the impact of international media coverage of Australia's position on Indigenous people, migrants and asylum seekers on other nations' images of Australia. In this experiment we examined whether there was any basis for such concerns by considering the short-term impact of negative TV coverage of Australians on Canadian viewers. A questionnaire provided baseline data on Canadian students' perceptions of Australians and Australian race relations. Four months later, the students were assigned to one of three conditions that varied media contact with Australians. Students viewed one of two television programs (about right-wing political independent, Pauline Hanson, and her emotive criticisms of Aborigines and Asian immigrants or about an ethnically-mixed group of young Australians and their positive sense of cultural identity), or they viewed no program (no contact control). Results indicated that both positive and negative media coverage of Australians affected Canadians' views of Australia in the short-term. In particular, negative coverage (of Hanson) promoted less favourable views of Australians and Australian race relations over time and relative to the positive media and no media control conditions. The media's role in shaping international images is 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 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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.179
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.395
Teacher spread0.355 · 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 teacher head, 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

Citations5
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueAustralian Journal of PsychologySame topicSocial Media and PoliticsFrench-language works237,207