Bibliographic record
Abstract
To count as democratic, social systems must empower the inclusion of people affected by collective endeavours to participate in practices that contribute to self-development and self- and collective-rule. As a political practice, talking is important because it is an essential tool for enacting social identities and enabling self-development. Talking is how people think through their preferences, and helps people relate private preferences to those collective opinions and agendas that enable collective rule. However, formal barriers (such as legal restrictions) can entail exclusions that prevent disempowered social group members from participating in, or influencing practices – including talk – that contribute to self-development and self- and collective-rule. Furthermore, even in the absence of formal barriers to social and political participation, the historical legacy of structural inequality can pattern social cognition and contribute to internal exclusions that engender asymmetries in political participation and influence, including asymmetries in discursive participation and influence. I address the empirical question of whether inequality shapes social cognition to engender asymmetries in social group members’ discursive participation and influence in two analyses. In my empirical chapters, I turn my attention from a broader concern with social inequality and narrow my focus to gender inequality. In my first empirical chapter, I use Canada Election Studies (2015) data to show there is an ongoing gender gap in discursive participation. In my second empirical chapter, I use data from an original vignette experiment to show that when women do talk politics, they have less influence than men. Finally, I suggest practices and institutions to help neutralise discursive inequalities, so democratic systems can come closer to the ideal of discursive equality, or equal participation and influence in communicative processes of self-development and self- and collective-rule.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".