Brand Jane - Finch: A Critical Discourse Analysis of Print Media Discourse on a Toronto Low Income Community
Bibliographic record
Abstract
The theoretical frameworks of critical discourse analysis (CDA), framing and branding are fused in this study to create a variant framework through which news discourse on Jane-Finch, a low-income community in Toronto, Canada is filtered. The approaches of CDA, framing and branding intersect around language and power and are therefore beneficial for combining in any analysis that seeks to identify the socio-cultural influences on the text, such as the reiteration of dominant discourses within the text. This research examined news articles selected from The Toronto Star and the Globe and Mail, two mainstream Canadian newspapers with the highest circulation. The news articles show that Jane-Finch is portrayed in a negative and stereotypical way by both newspapers. These findings support the view that news discourse is as ideologically-bound as other forms of discourse, despite claims to its objectivity. As a consequence of being ideologically-bound, a fundamental attribution error is enacted in news coverage on Jane-Finch, as coverage repeatedly attributed the problems in Jane-Finch to race and immigrants (internal factors) and ignored the more substantive contributing factors of discrimination, structural constraints, poverty and joblessness (external structural factors). More generally, the analysis shows that news about Jane-Finch tends to be negative and therefore obscures any positive developments in the community. Sustained repetition of the same news stories brands the community with a negative reputation and promotes a false stereotype of its residents. Not only is news capable of agenda-setting (bringing certain issues to the forefront), but in repeatedly showcasing issues the same way all the time, a 'branding effect' (similar to the process in corporate branding) is enacted. A 'domino effect' results in that negative branding of Jane-Finch affects property values, prevents educational and job opportunities for its residents and reduces economic development of the area. Ultimately, this can cause misrecognition of the 'real' problems affecting the community, preventing the development of effective community and policy responses to these issues. The thesis not only contributes knowledge in the area of branding and the news but also contributes in the area of policy-making related to low-income communities and the poor. Policy-makers need to address external structural factors affecting these areas and pay less attention to the 'sensational' areas showcased by the media. For example, because of the focus on crime in the news media, policymakers may think more stringent crime laws are needed when if other issues, such as ensuring jobs for the young, are addressed, youth crime would lessen.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.024 | 0.020 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".