Gray hair and pink slips: An analysis of Twitter responses to gendered ageism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it