What Makes the Good Life Good? An Investigation into the Nature of Happiness
Bibliographic record
Abstract
My research focuses on the nature of happiness as presented by contemporary philosopher Fred Feldman in his recent work, What is This Thing Called Happiness?(2010). Feldman offers an innovative theory of happiness that suggests happiness is contingent on a subject’s capacity to acquire more pleasure than displeasure in his or her surroundings. Feldman’s model is a valuable contribution to the study of happiness because it calls attention to the power a subject has in determining her own happiness. Like many of his predecessors including Aristotle, Feldman describes the happy life as the Good Life. However, where Aristotle measures the Good Life in terms of virtue, Feldman uses welfare as his metric. A problem with Feldman’s approach is that he rejects Aristotle’s arguments for why happiness is the Good without providing a suitable alternative. In this paper, I address the limitations of Feldman’s model by examining the implications of this omission. I will argue that Feldman lacks a conclusive argument for why the Good Life consists of welfare. Most significantly, Feldman’s account is problematic because it leads to the unusual conclusion that many of us are not in fact pursuing the Good Life for fear of becoming moral monsters. By reintroducing virtue into our description of happiness, and arguments for why happiness is our greatest good, I think that we can rescue contemporary theories of happiness from the repugnant moral conclusions that I have suggested are present in Feldman’s work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".