Beyond backlash: #MeToo and female unemployment trends
Bibliographic record
Abstract
In 2006, American activist Tarana Burke initiated the Me-Too movement to empower women to stand up against sexual abuse. However, it was not until October 2017, when actress Alyssa Milano encouraged survivors to share their stories on social media that the movement gained widespread attention, leading to the viral spread of the hashtag #MeToo. This symbol of solidarity among survivors of sexual harassment and assault quickly became a global phenomenon. As with any movement that challenges the status quo, Me-Too faced significant backlash. By early 2018, some journalists, writers, and corporate businesspeople in the US voiced concerns that the movement might inadvertently lead to reduced hiring of women, potentially fostering a climate of fear and silence among victims. Contrary to these fears, we argue that the Me-Too movement could improve women's employment prospects. Utilizing transfer function and intervention analysis on monthly US female unemployment rate data from January 1995 to February 2020, we explore the immediate and dynamic effects of the movement's surge in 2017. Our findings indicate that #MeToo has not negatively impacted female employment chances. This study highlights the potential of using nuanced, high-frequency time-series data to inform research in sociology, economics, and related fields.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".