Visual ’othering’ of immigrants in Canadian and Finnish online newspapers from 2016
Bibliographic record
Abstract
This thesis aims to investigate and compare how immigrants in Canada and Finland are visually ‘othered’ in media. Critical events in 2016 heightened the discussion of immigration, further emphasizing media’s importance of representing this population to the public. Visual representations of the ‘other’ can come in subtle forms yet perpetuate imagined communities of ‘we’ and ‘they’ in major ways. It is therefore becoming more imperative to conduct research on processes of ‘othering.’ This thesis uses visual framing analysis (VFA) on leading newspapers in Canada and Finland, a combined total of 271 images were collected for analysis from Canada’s Toronto Star and Finland’s Helsingin Sanomat. These images were examined for visual frames of the ‘other’ by measuring communication with the viewer, spatial proximity, depiction with others or as individuals, social interaction and vertical and horizontal points of views. The results revealed clear distinctions of framing immigrants as the ‘other’ in both newspapers. Canadian media ‘othered’ immigrants half the amount as Finnish media, which ‘othered’ immigrants in nearly all codes examined. The findings of this research suggests how a further understanding of complex identities and visual literacy is key to understanding diversity and culture and disintegrating a sense of the ‘other.’
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".