The Limits of Political Efficacy: Educating Citizens for a Democratic Society
Bibliographic record
Abstract
Justin is not alone. In a recent study of high school seniors from California, for example, only 28% agreed that “I think people in government care about what people like me and my family need” (Kahne and Middaugh 2005). Related findings are common. A survey by the National Association of Secretaries of State, for example, revealed that two-thirds of all young people agreed that “our generation has an important voice, but no one seems to hear it.” Moreover, those youth who were least trusting were also the least likely to vote, to believe that government can affect their lives, or to pay attention to politics (National Association of Secretaries of State 1999). These findings, combined with numerous other indicators that show low and in many cases declining civic and political participation, indicate that forms of engagement required for a participatory democracy to thrive are in need of attention (Macedo et al. 2005). The initial research for this essay was generously supported by a grant from the Surdna Foundation. Subsequent research and writing was generously supported by the Social Sciences and Humanities Research Council of Canada. We also wish to thank Melinda Fine, Barbara Leckie, Tobi Walker, and James Youniss for helpful feedback on earlier drafts. The authors are solely responsible for any and all conclusions.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".