Proast: From <i>A Third Letter concerning Toleration in Defence of the Argument of the Letter concerning Toleration, Briefly Considered and Answered</i> (1691)
Bibliographic record
Abstract
Where I say that ‘force may indirectly and at a distance do some service, etc.’ you say you do not understand what I mean by ‘doing service at a distance towards the bringing men to salvation, or to embrace truth, unless perhaps it be what others, in propriety of speech, call by accident’. But I make little doubt but all other men that read the place, do well enough understand what I mean by those words; even such as do not understand what it is to ‘do service by accident’. And if by doing service by accident, you mean doing it but seldom and beside the intention of the agent, I assure you that is not the thing that I mean when I say force may indirectly and at a distance do some service. For in that use of force which I defend, the effect is both intended by him that uses it, and withal, I doubt not, so often attained as abundantly to manifest the usefulness of it. ‘But be it what it will,’ say you, ‘it is such a service as cannot be ascribed to the direct and proper efficacy of force. And so,’ say you, force indirectly and at a distance may do some service. I grant it: Make your best of it. What do you conclude from thence? That therefore the magistrate may make use of it? That I deny. That such an indirect and at a distance usefulness will authorize the civil power in the use of it, that will never be proved.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".