Bibliographic record
Abstract
To Valentine and his monks: On account of those who preach and defend human free choice in such a way that they dare to deny and try to get rid of the grace of God – the grace by which we are called to Him and are set free from our evil deserts, and through which we acquire good deserts by which we might attain eternal life – I have already examined a number of points and written about them, as far as the Lord found worthwhile to grant to me. But since there are some people who defend the grace of God in such a way that they deny human free choice, or who hold that free choice is denied when grace is defended, I have for this reason been inspired by our mutual charity to take the trouble to write something on this issue to Your Charity, brother Valentine, and to the others who serve God with you. Word about you has reached me, brothers, from some members of your community who came to me (and by whom I have sent along this work), that there are disagreements among you on these matters. Therefore, dearly beloved, I advise you first to thank God for what you do understand, so that the obscurity of the question not disturb you. As for anything still beyond the reach of your mind's effort, pray for understanding from the Lord while maintaining peace and charity among yourselves.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".