Bibliographic record
Abstract
<p>This paper’s starting point is the popular thesis that emotions are constituted by experiences of value. This thesis raises what I call the value question: what exactly are these values that emotions are supposedly about? ‘Value’ here is understood broadly to include not only properties such as being good, bad, fearsome, dangerous, etc. but also being right, wrong, a reason, etc. In my view, the value question hasn’t received the concentrated attention that it deserves (though there are some notable exceptions), perhaps because it isn’t immediately clear how to adjudicate competing answers. I argue, however, that Ronald de Sousa has developed two important ideas which can help us to make progress. These two ideas are as follows: (i) emotions help to solve the “frame problem” by controlling what strikes us as salient in deliberation, and (ii) emotions are Janus-faced, looking outward toward the world and inward toward the self. Careful consideration of the value question in light of these two proposals favours the view that emotions are about the following value: the emoter’s reasons to do things.</p> <p> </p>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.014 |
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; both teacher heads agree on what is shown here.
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".