Bibliographic record
Abstract
It is widely accepted that emotions have something to do with values. The major task of contemporary philosophy of emotion is to say precisely what that something is. How exactly are emotions related to evaluative properties? Unsurprisingly, there are various ways they may be related. First, emotions might themselves be bearers of value. It might be a good thing to be afraid, sad, or joyful in certain circumstances. And of course, there are various ways emotions might be valuable. One way – but probably not the only way – they might be valuable is in providing us with information about the world, and in particular about further values. A second way, therefore, emotions might be related to values, is by helping us apprehend (or ‘access’) such values, thereby playing an important – perhaps indispensable – role in the acquisition of evaluative knowledge. Yet a third way emotions might be connected to values is by constituting them in some way. Perhaps, what it is for something to have some value, or for it to matter, is for it to stand in a certain relation to our emotions.
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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 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; 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".