Bibliographic record
Abstract
Abstract. In The Really Hard Problem, Owen Flanagan maintains that accounting for meaning requires going beyond the resources of the physical, biological, social, and mind sciences. He notes that the religious myths and fantastical stories that once “funded” flourishing lives and made life meaningful have been epistemically discredited by science but nevertheless insists that meaning does exist and can be fully accounted for only in a form of systematic philosophical theorizing that is continuous with science and does not need to invoke myth. He sees such a mode of thought as a new, empirical‐normative science, which he labels eudaimonistic scientia, that evades the disenchantment produced by natural scientific accounts of meaning. I argue that such an empirical‐normative science does not provide us with a scientific account of meaning but is itself simply another way of making sense of one's life that is open to scientific explanation. Such an explanation will be deflationary in the sense that it presumes no greater scheme of things for meaning beyond the span of human existence (collective and possibly individual) but not disenchanting in that it does not explain away the flourishing lives human persons and communities create for themselves.
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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.090 |
| Scholarly communication | 0.008 | 0.026 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".