Evaluation and Motivation Part Company? The Problem of <i>Akrasia</i>
Bibliographic record
Abstract
The scholastic identification of desiring and conceiving to be good, of motivational and evaluative attitudes, faces a challenge from cases of akrasia . In cases of akrasia , agents are not motivated in accordance with comparative evaluations. An akratic agent will think that A is better than B yet pursue B . In these cases, the agent's motivational states seem not to fall in line with his evaluative states. It is not completely uncontroversial that accidie and akrasia are indeed possible, at least if described as cases in which the agent's motivation does not correspond with his evaluative judgments. However, denying this possibility seems like denying the phenomena; we at least seem to confront instances of these kinds of behavior often enough. The existence of phenomena that correspond to these descriptions of akrasia seems to be a major argument against the scholastic view in the context of intentional explanations and in favor of separatist views, views that allow motivation and evaluation to come completely apart. The aim of this chapter is to show not only that the scholastic view has the right tools to account for akrasia but also that it accounts for the phenomena better than separatist views. THE PROBLEM WITH AKRASIA In the Protagoras , Socrates says that most people maintain that there are many who recognize the best but are unwilling to act on it.… Whenever I ask what can be the reason for this, they answer that those who act in this way are overcome by pleasure or pain.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".