Bibliographic record
Abstract
iii .1.1 We have stated in general terms that the virtues involve mean points, that they are concerned with decision, and that their opposites are vices, and what these are. Now let us take the virtues individually and discuss them in sequence, beginning with courage. iii .1.2 It is pretty much universally held both that being courageous is about one’s fears, and that courage is one of the virtues. Earlier, in our list, we distinguished fear and recklessness as opposites, and in fact these are in a way contrary to one another. iii .1.3 So clearly those who are described in terms of these states will likewise be contrary to one another – the coward, who is described in terms of being more fearful and less confident than one ought, and the reckless person, described in terms of being such as to be less fearful and more confident than one ought. This is where the term is derived from: the reckless person is named derivatively after recklessness. iii .1.4 Courage is the best disposition with respect to fear and confidence. One should be neither like the reckless, who display deficiency with regard to the former and excess with regard to the latter, nor like cowards, who do the same, except not in the same respects but the other way around – they have a deficiency of confidence and an excess of fear. Hence it is clear that courage is the mean disposition between recklessness and cowardice, this being the best.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.227 | 0.116 |
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".