Bibliographic record
Abstract
Psychologists, cognitive experts, and philosophers alike have long been interested in why people go against their better judgement: why do people do y when they know all things considered x is better to do? Why does a student go out rather than working on his/her essay; the completion of which they know to be their top priority. The purpose of the presentation is twofold. First and foremost, it hopes to make digestible to the everyday thinker the philosophical research that has been conducted on this matter. Oftentimes when philosophers release ground-breaking work their paper is too dense and prose-filled to be comprehensible by non-philosophers. Secondly, this presentation hopes to locate not only the source of irrational action, which it finds to be the passions, but also present a solution to the problem of irrational action, which it argues is self-reflection. It is by having an honest and open conversation with oneself about 1) one's goals, aims, and ambitions and 2) one's values [what kind of person they want to be], that one is able to turn away from weakness and to act rationally. Moreover, this paper argues that acting rationally is an ongoing process, where the individual must continually assess their actions to ensure they are falling in line with their aims and values.
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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.054 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".