Bibliographic record
Abstract
Philosophers such as Plato, Aristotle, Kant, Brentano, Moore, and Chisholm suggest marks of intrinsic value. Contemporary philosophers such as Christine Korsgaard have insightful discussions of intrinsic value. But how do we verify that some specific thing really is intrinsically valuable? I propose a natural way to test for intrinsic value: first, strip the candidate bare of all considerations of good consequences; and, second, see if what remains is still a good thing. I argue that we, as ordinary human beings, have an astonishingly difficult time completing this test for plausible candidates. More precisely, for us as we are, it seems that the conditions for completing the first step of the test militate against the conditions for completing the second step. I conclude, then, that we have a good reason to think that we cannot verify whether or not particular things are intrinsically good. I explore some implications and I consider a number of important objections.
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 | 0.077 | 0.271 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.051 |
| Scholarly communication | 0.006 | 0.027 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".