Generational change in gender gaps in political behaviour and attitudes: the roles of modernisation, secularisation, and socialisation
Bibliographic record
Abstract
This thesis examines to what extent there are generational differences in gender gaps in political behaviour and attitudes, and what explains this generational variation. Generations differ considerably on factors such as women’s role in the family and the workplace, gender inequality, and formative experiences, and I argue this leads to different gender gaps for different generations. I examine such generational variation in gender gaps in vote choice, left-right self-placement, attitudes towards spending and redistribution, and attitudes towards gender-egalitarianism. Broad cross-national trends in Europe and Canada are identified, as well as country-specific patterns using Britain and the US as case studies. This thesis finds that generally, in the countries studied, men are more left-wing than women in older birth cohorts, whilst women are more left-wing than men in younger birth cohorts. This ‘gender-generation gap’ is produced through processes of modernisation, especially secularisation. In addition to this broad trend, the political context or zeitgeist during a generation’s formative years produces gender gaps in both vote choice and attitudes that differ between generations according to this socialisation experience. The influences of modernisation and such political socialisation interact to create complex patterns of generational variation in political gender gaps that differ across political contexts. For example, in the British case, women of younger cohorts are not more left-wing in their vote choice than men. These results suggest that we should focus on gender gaps at the level of generational subgroups in order to fully understand political differences between men and women. Furthermore, they predict that gradually, the gender gap where women are more left-wing than men will grow over time through generational replacement. However, they also indicate that this will not occur in all contexts, and that more work needs to be done to understand how the political context shapes gender gaps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 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".