Bibliographic record
Abstract
Anyone who works on a subject over a period of more than twenty years owes many debts of gratitude. It was in 1971 in his ‘Theological Controversies’ course at Harvard Divinity School that Arthur McGill proposed that we should study the subject of justification on the one hand in Luther, on the other at Trent. I believe that I was immediately captivated. (The second-hand copy of John Dillen-berger's Selections from Luther's writings – which I bought thinking I should only need it for a week – is still with me and in dilapidated condition.) When some years later I came to write a doctoral thesis I had no doubt as to what the topic should be (though I had some difficulty in convincing my teachers). Then there was a day when Arthur McGill asked how Kierkegaard related to all this. I replied, as though it was self-evident, that his was the best solution I had encountered in the history of Western thought to the split between Catholic and Lutheran. ‘There’, he said, ‘is your thesis’. In the years that I have thought about this topic, first writing a thesis and then more recently this book, many people have talked with me about my work. In 1976 I went to see Philip Watson, whose writing on Luther (at a time when few were interested) remains a landmark. Trained as he was in motif research, he profoundly influenced my own reading of Luther.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.522 | 0.314 |
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".