Does Compulsory Voting Lead to More Informed and Engaged Citizens? An Experimental Test
Bibliographic record
Abstract
Abstract. Does compulsory voting lead to more knowledgeable and engaged citizens? We report the results from a recent experiment measuring such “second-order effects” in a compulsory voting environment. We conducted the experiment during the 2007 Quebec provincial election among 121 students at a Montreal CEGEP. To receive payment, all the students were required to complete two surveys; half were also required to vote. By comparing knowledge and engagement measures between the two groups, we can measure the second-order effects of compulsory voting. We find little or no such effects. Résumé. Le vote obligatoire augmente-t-il le niveau d'information et l'engagement politique des citoyens? Nous présentons les résultats d'une expérience mesurant de tels « effets secondaires' » dans un environnement caractérisé par le vote obligatoire. Nous avons mené une expérience auprès de 121 étudiants d'un cégep montréalais lors de l'élection québécoise de 2007. Afin de recevoir une somme d'argent, les étudiants n'avaient qu'à compléter deux questionnaires; une moitié des participants devait en plus voter le jour de l'élection. En comparant le niveau d'information et l'engagement entre les deux groupes, nous pouvons mesurer les effets secondaires du vote obligatoire. Notre expérience révèle que le vote obligatoire a peu ou pas d'effet sur les connaissances et la participation.
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 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".