Bibliographic record
Abstract
IntroductionThere is a great deal of interest and research on gender gap in political knowledge. Three basic questions often frame this literature: Is there a gender gap in political knowledge, why is there a gap, and are sources of political knowledge same for men and women? Research suggests that there is often a gender gap in political knowledge. Scholars disagree about its source. For some, it is a methodological artifact (e.g., Mondak and Anderson 2004). For others, it reflects differences in characteristics, such as level of interest in politics, or differences in return for characteristics, such as education (Dow 2009). Most of literature on political knowledge, however, focuses on US. We seek to add to literature on political knowledge by addressing following questions: What are sources of political knowledge and are they same for men and women in Poland, is there a gender gap in political knowledge in Poland, and are patterns found in a consolidating democracy similar to those found in established democracies?We use nationally representative survey data to examine how motivation, ability, and opportunity influence men's and women's knowledge of twelve national political parties- that is, whether they could correctly indicate if each party was currently in ruling coalition. We predict whether or not respondents answer 'don't know' to entire question set as well as whether or not they were able to answer all twelve questions correctly. Independent variables include political interest (motivation); educational attainment and cognitive ability (ability); household income, access to cable or satellite TV, internet access, voting experience, employment status, religious attendance, size of place of residence, marital status, and having children (opportunity); and controls (age and self-esteem). We use multiple imputation to handle missing data and estimate interaction models to test for differences in coefficients for women and men.This paper makes several contributions to literature on political knowledge. First, it examines gender gap in political knowledge in a new political context-Poland. There are a few single-country studies that focus on gender gap in political knowledge outside of US-for example, in Belgium (Hooghe, Quintelier, and Reeskens 2006), Britain (Frazer and Macdonald 2003), Canada (Stolle and Gidengil 2010), and China (Tong 2003). We hope that our analysis will help to establish whether or not differences in levels and sources of political knowledge are similar to patterns found in other countries despite differences in political context. Second, our data include measures of both educational attainment and cognitive ability and cognitive ability measure is based on an intelligence test rather than interviewer assessment. Third, we examine political knowledge as a two-stage process by first predicting whether respondents answered knowledge questions or simply indicated that they 'don't know' for entire question set. In second step, we examine differences in knowledge among only those providing 'yes' or 'no' answers for each party.Political KnowledgeDelli Carpini and Keeter (1996) define political knowledge as the range of factual information about politics that is stored in long-term memory (p. 10). The broad categories of political knowledge include: 'rules of game,' 'players,' and 'substance' (e.g., domestic politics) (Delli Carpini and Keeter 1996). Most scholars argue that motivation, ability, and opportunity explain why some people know more about politics than others (see Delli Carpini and Keeter 1996, Chapter 5; Dow 2009: 120; Luskin 1990: 334).First and foremost, political knowledge depends on motivation. Without interest in politics, people would not pay attention to politics nor would they retain any political information. The level of political knowledge is also rooted in ability. …
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".