Moral Cultivation and the Quantified Self: Assessing the Self Understanding of Data Profiles Generated by AI with a Virtue Ethics Approach
Bibliographic record
Abstract
Supporters of personal data collection and analysis contend that data profiles generated from AI algorithms represent a desirable pursuit for the quantified self. Proponents of the quantified self claim that AI-generated data profiles represent a more objective and truthful account of individual lives. They also argue that the quantified self fosters human flourishing by supplying individuals with data-informed accounts about their lives. First, I will trace the technological origins of the quantified self. Second, the first claim will be critiqued by demonstrating that the quantified self presents a reduced and subjectively abstracted picture of human life. Third, the second claim will be questioned, from a virtue ethics approach, to show how the quantified self’s reduced concept of self-examination is detached from self-cultivation. Fourth, a neo-Aristotelian virtue ethics framework will be applied to argue that the self-knowledge sought by the quantified self hinders agents’ practical reasoning.
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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 | 0.036 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.035 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".