Personal Autonomy for the Survival of Community in Pluralist Liberal Democracies
Bibliographic record
Abstract
IntroductIonThis essay will provide a new approach to the much-discussed tension between education for personal autonomy and the educational concerns of conservative Christian communities.One can find a wide variety of interpretations of "personal autonomy" in political, ethical, and educational literature.The conception of autonomy I wish to address here is autonomy as one's ability to make inquiries into various conceptions of the good life, and choose to pursue that life which seems to speak most deeply to one's fundamental needs and interests.1 Some have critiqued this account of autonomy on the grounds that one cannot inhabit a "god's eye view" outside of socio-historical contexts and traditions in order to "autonomously" choose a given conception of the good life. 2 However, there have been compelling responses to this objection.3 The autonomous self is not one that is somehow divorced from social attachment and influence, rather it is "a cluster of substantive attachments and projects."4 Such a self is "revocably encumbered." 5 All deliberation is bound by the particulars of language and socio-historical context.However, regardless of this boundedness, one can still rationally reflect and revise commitments in a piecemeal way by critiquing some commitments in the light of others.Rather than being an obstacle to autonomy, these "encumbrances" provide the context and tools with which to pursue knowledge.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.031 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".