Bibliographic record
Abstract
The media plays an important role in the process of shaping attitudes about controversial issues such as the arrival of refugees to Canada. The first aim of this research was to investigate how the Canadian newsprint media portrayed one noteworthy event involving the arrival of refugees to Canada: the arrival of the Tamil refugee boat to British Columbia in August of 2010. A media content analysis revealed that the overall portrayal of refugees in the Canadian press in response to this event was mixed. On the one hand, refugees were perceived either as bogus claimants or as criminals/terrorists. On the other hand, refugees were also perceived as victims. The second aim of this research was to investigate the effect of these media depictions on the automatic dehumanization of refugees. Results showed that exposing participants to editorials depicting refugees as bogus, terrorists or, surprisingly, as victims activated the automatic dehumanization of refugees. In contrast, exposing participants to an editorial with neutral, factual information about refugees did not activate the automatic dehumanization of refugees. The results are discussed in the context of the implicit social cognition model of media priming (Arendt, 2013). The results suggest that the best way for the media to approach controversial issues such as the arrival of refugees to Canada may be to engage in factual, non-biased journalism. The present research is the first demonstration that media portrayals of refugees can cause the automatic dehumanization of refugees.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".