Bibliographic record
Abstract
The Canadian philosopher Charles Taylor is one of the most influential political theorists today. The concept of the ‘good’ constitutes the core in his philosophical and political thoughts, and is the object of my Thesis. Taylor believes that the retrieval of the good is the only way to resolve the identity crisis of modernity. To carry out the retrieval of the ‘good’ in political realm, he suggests that we should establish the patriotic liberal regime together with politics of recognition. He believes one could be able to deal with the conflicts of hypergoods between political and cultural communities by common deliberation. The ethical dimension of Taylor’s concept of the good is closely examined in Chapter 2. In Chapter 3, I demonstrate that the idea of practical reason is the link between Taylor’s concept of the good and his ideas of patriotic liberal regime and the politics of recognition. I suggest that in rejecting the procedural principle of liberalism, Taylor’s objective is to integrate republicanism with multiculturalism. However, he ultimately fails to achieve this objective because Taylor’s approach cannot solve the conflict between hypergoods. Moreover, Taylor’s conceptualization makes the conflict even more intense. However, though Tailor’s theory cannot solve the contradiction, his idea of reconciling the disputes by rational deliberation and the claim of the politics of recognition is still helpful to us.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".