Bibliographic record
Abstract
While it is often argued that political participation can be measured by both ex ante (intention) and ex post (report) methods ( Castillo, Miranda, Bonhomme, Cox, & Bascopé, 2014 ; Ekström & Östman, 2013 ; Hooghe & Wilkenfeld, 2008 ; Persson, 2014 ), some authors claim that intention is a poor predictor of actual participation ( Brady, 1999 ; Furnham & Gunter, 1989 ; Norris, 2004 ) and have even found evidence that the link between intention and behavior is quite weak ( LaPiere, 1934 ). However, to our knowledge, no studies have yet explored how these two measurements of political participation are linked: How strongly are intended and reported political participation correlated, and do the same factors influence the two measures? Although some authors have explored the relationship between intention to vote, reported vote, and validated vote ( Achen & Blais, 2016 ; Granberg & Holmberg, 1991 ), such research is lacking for other political participation activities. For example, voting studies generally find that there is a strong correlation between intention to vote and reported turnout. However, the question remains if the pattern is the same with regard to overall political participation. Furthermore, these studies suggest that the same variables influence intended and reported turnout, but not with the same magnitude. Unfortunately, no such information seems to be available yet for other forms of political participation.
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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 | 0.008 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".