GRAY HAIR AND PINK SLIPS: AN ANALYSIS OF TWITTER RESPONSES TO GENDERED AGEISM
Bibliographic record
Abstract
Abstract Popular, long-time Canadian broadcaster, Lisa LaFlamme, was fired in August 2022, with some observers suggesting that CTV National News did not renew her contract because she had “let her hair go gray” during the pandemic. Over the next several months, an international public outcry ensued on Twitter. Our study involved an analysis of over 400 of these tweets. Analyses revealed that over two-thirds of tweets indicated support for LaFlamme’s position, with nearly all the remaining tweets indicating neutral rather than negative positions. Frequent themes in the supportive tweets included assessments of her firing as undeserved and the actions of the news corporation as unjust. Calls to boycott the corporation also appeared. Other themes found in supportive tweets included ageism and sexism; however, references to sexism were somewhat more frequent than those to ageism. A theme appearing not only in supportive but also in neutral or oppositional tweets centered on beauty standards, with supportive tweets more likely to critique these standards’ sexist and ageist underpinnings. Our study extends the literature on gendered ageism by attending to collective responses to it. In contrast, prior studies have focused on individual-level experiences of and responses to gendered ageism. Our study also contributes to the hashtag activism literature, which has given little attention to efforts to dismantle age inequality.
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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 | 0.002 | 0.009 |
| 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".