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Record W4386176406 · doi:10.36923/jicc.v10i2.505

Changing Stereotypes in Iran and Canada Using Computer Mediated Communication

2010· article· en· W4386176406 on OpenAlexaffabout
Mahin Tavakoli, Javad Hatami, Warren Thorngate

Bibliographic record

VenueJournal of Intercultural Communication · 2010
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsCarleton UniversitySt. Francis Xavier University
Fundersnot available
KeywordsPsychologyThe InternetQuality (philosophy)Social psychologyComputer science

Abstract

fetched live from OpenAlex

As part of a university course activity, one group of Canadian and one group of Iranian students were randomly partnered to exchange e-mail messages via the Internet for seven weeks. Before beginning their correspondence, all students completed a questionnaire measuring their stereotypes, attitudes, and knowledge about the people and culture of their prospective e-pals. Students from both countries then exchanged messages and photos. In addition, students within each country met with one another to discuss their e-pal exchanges each week. At the end of seven weeks of e-mail exchange, all students again completed the original questionnaire. Pre-posttest changes in attitude, stereotypes, and knowledge about the culture of e-pals show that attitudes of participants towards people from the other country became more favourable, even though their judgments of the similarities between two cultures remained unchanged. Negative stereotypes changed towards more realistic ones. Attitude change was affected by the quality, topic, and frequency of e-mail exchange. Knowledge of participants about different aspects of the other culture became more complex and realistic over time. However, for many aspects of each culture, there was no consistent relationship between raising the level of knowledge and a change in attitude.

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)0.000
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 score0.426
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.297
Teacher spread0.275 · 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

Citations9
Published2010
Admission routes2
Has abstractyes

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