How Bell Canada Capitalises on the Millennial: Affective Labour, Intersectional Identity, and Mental Health
Bibliographic record
Abstract
Abstract Since 2010, the large telecommunications company, Bell Canada, has invited Canadians to “break the stigma” around mental illness through a campaign called #BellLetsTalk. The campaign claims to donate millions to mental health initiatives, aiming to also “start a conversation” about mental health online. In large part, the Bell Let’s Talk campaign depends on the position of the millennial as a social media user with a real stake in conversations revolving around mental health. I highlight how the term “mental health” is often correlated to normative affect and behaviour, pointing to the importance of an intersectional understanding of mental health. Colonialism is also at play here, as the Bell campaign donates to Indigenous communities, but fails to address how psychiatric intervention is often a colonial process in itself. Through a feminist and critical disability studies lens, I critique Bell for its seemingly apolitical ad campaign, arguing that it bolsters normative narratives around psychological distress and its place in neoliberal corporations and colonial Canada.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.054 | 0.064 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".