An Exploration of Transcultural Othering in the Print Media: Asian Communities During COVID-19 in Canada and the US
Bibliographic record
Abstract
This thesis explores the transcultural othering of Asian communities in the Canadian and American print media during the COVID-19 pandemic.Using content analysis, I analyzed 226 magazine articles published by Maclean's and Newsweek between February 1st, 2020 and April 30th, 2021.My analysis found 17 othering mechanisms were used against Asian communities.China was the most frequently discussed country within the data and contributed to a negative portrayal of Asians overall.Political articles were identified as the primary source of exclusionary othering towards Asians, whereas articles on COVID-19 were mostly inclusionary.This thesis contributes to the extant literature by further theorizing how and where transcultural othering occurs.It challenges established notions of the mainstream media scapegoating illnesses on transcultural communities during pandemics as a predominant source of exclusionary othering during COVID-19 and suggests that the print media's political coverage was a greater source of exclusion.Implications and limitations are discussed.Appendix C Code Co-occurrences ........
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".