Fitting-Attitude Analyses and the Relation Between Final and Intrinsic Value
Bibliographic record
Abstract
This paper examines the debate as to whether something can have final value in virtue of its relational (i.e., non-intrinsic) properties, or, more briefly put, whether final value must be intrinsic. The paper adopts the perspective of the fitting-attitude analysis (FA analysis) of value, and argues that from this perspective, there is no ground for the requirement that things may have final value only in virtue of their intrinsic properties, but that there might be some grounds for the alternate requirement that final value be grounded only in the essential properties of their bearers. First, the paper introduces the key elements of the FA analysis, and sets aside an obvious but unimportant way in which this analysis makes all final values relational. Second, it discusses some classical counterexamples to the view that final value must be intrinsic. Third, it discusses the relation between final, contributive, and signatory value. Fourth, it examines Zimmerman’s defense of the requirement that final value must be intrinsic on the grounds that final value cannot be derivative. And finally, it explores the alternative requirement that something may have final value in virtue of its essential properties.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".