Gray hair and pink slips: An analysis of Twitter responses to gendered ageism
Bibliographic record
Abstract
When Canadian broadcaster, Lisa LaFlamme, announced in August 2022 that CTV National News did not renew her contract, some observers suggested that the corporation's decision resulted from LaFlamme's choice to "let her hair go gray" during the pandemic. An international public outcry ensued on Twitter. Our study involved an examination of these tweets (n = 440). Analyses revealed that approximately 80 percent of tweets indicated opposition to LaFlamme's dismissal, while only 2 percent indicated support and 18 percent indicated a neutral position. Among tweets expressing opposition, the most common justification, found in 79 percent of these tweets, centered on assessments of the employer's decision as poor. The frequency of all other justifications for opposition was considerably lower, with only 26 percent of these tweets mentioning ageism, 22 percent mentioning sexism, and 20 percent mentioning a general sense of unfairness to LaFlamme. These findings suggest the salience of capitalist logics in shaping how the public frames gendered ageism in the workplace. Our analyses also suggest a view of responses to this inequality as personal bodywork choices. Together, these framings reflect a more individual- than structural-level critique of gendered ageism, knowledge of which can inform efforts to dismantle it.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".