Moral Agency in the Modern Age: Reading Charles Taylor through George Grant
Bibliographic record
Abstract
A shared concern with the nature of moral agency in modernity makes George Grant a useful interlocutor for Charles Taylor. Taylor sees human agency as constituted by moral affirmations, as given in the process he calls “strong evaluation.” He examines the “moral sources,” including reason, nature, and God, that inform the modern identity, explaining “technological society” in light of these affirmations. Grant’s analysis of technology shares much with Taylor’s, but underlines the irreducibility of technological civilization’s “will to mastery” to any of Taylor’s moral goods. This “will” is a distinctive and constitutive affirmation of modern agents (akin to a Taylorian “source”); but it is fundamentally amoral and, indeed, corrosive of morality. Reading Grant thus offers an important corrective to Taylor’s historical account of affirmation in modernity, while challenging his theory of identity as necessarily constituted by moral goods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".