Bibliographic record
Abstract
To what extent should the child’s point of view be included when a political community endeavors to make just decisions, and why? Democrats are committed to a principle of political inclusion grounded in equal respect for persons. Yet we regularly deny children the right to vote and we often just assume that the citizens doing the hard work of democratic deliberation are adults. As I will show, electoral conceptions of democracy can plausibly reconcile this tension in a way that requires no serious adjustment to the principle of inclusion. However, I also argue that a similar reconciliation seems unavailable to deliberative conceptions of democracy, and this fact has implications for how deliberative democrats should understand political inclusion and its relationship to the aims of schooling. I do this by providing a broad overview of deliberative conceptions of democracy, with a focus on some fundamental epistemic features of these conceptions, to explain why deliberative democrats must take a different approach. I then look at different arguments for children’s deliberative inclusion and propose an account of my own. Finally, I use this account in order to offer a different perspective on the aims of schooling under deliberative conceptions of democracy.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".