Changing Stereotypes in Iran and Canada Using Computer Mediated Communication
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".