What Exactly (If Anything) is Wrong with Paternalism Towards Children?
Bibliographic record
Abstract
Theoretical and practical issues concerning the justification of paternalism towards children are widely debated in a variety of philosophical contexts. The major focus of these debates either lies on questions concerning the general legitimacy of paternalism towards children or on justifications of paternalism in concrete situations involving children (e.g. in applied ethics). Despite the widespread consensus that the legitimacy of educational paternalism in important respects hinges on its principled, temporal and domain-specific limitation (e.g., via a soft-paternalist strategy), surprisingly little has been said about conditions and criteria that determine what exactly (if anything) is morally wrong with paternalism towards children. This contribution aims to further the understanding of these normative issues by providing a critical analysis of the theoretical and methodological difficulties involved in developing context-invariant criteria for the identification of specific wrong-making features of paternalist rationales and paternalistically justified practices in cases involving children. I am going to show that the moral status of pro- and anti-paternalist reasons is much more context-sensitive than usually assumed by proponents of standard generalist justificatory strategies. In conclusion my argument is that a moral particularist and casuistic framework may offer an adequate theoretical alternative to make sense of the context-dependent wrongs (and rights) of educational paternalism.
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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 | 0.024 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.050 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".