Democracy, Free Elections, and Gender Equality as Perceived by Recent Immigrants
Bibliographic record
Abstract
Immigrants in a democratic host society are supposed to support democracy, participate in core democratic processes, such as elections, and uphold its fundamental values, including gender equality. However, it is not sufficiently explored what they really mean by democracy, how they perceive the relationship between democracy and its indisputable attributes, such as free elections and gender equality, and how the host society itself could contribute to these perceptions of democracy. This study aims to contribute to this knowledge. The data were collected in Quebec using original questionnaire completed by 127 adult immigrants. The results indicate that recent immigrants from nondemocratic countries primarily view democracy as a society based on the rule of law with strong social control. They may lack knowledge about free elections, considering them significantly more important to democracy than the possibility to vote for any political party. Contrary to expectations, recent immigrants view gender equality as essential for democracy, although this importance varies according to sphere. Immigrants' conceptions of democracy also signify the most salient characteristics of the social and physical environment of the host society as the most important democratic features. The study concludes that more welcoming host democratic societies may foster more favorable concepts of democracy among immigrants.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".