Modern interpretations of Romans : tracking their hermeneutical/theological trajectory
Bibliographic record
Abstract
Cristina Grenholm, Church of Sweden, and Daniel Patte, Vanderbilt University, Postlude: Playing Scriptural Criticism in Multiple Keys on Romans Eugene TeSelle, Vanderbilt University, How Kant Influenced Modern Theological Readings of Romans Kurt Richardson, McMaster University, Schleiermacher and Romans Dr Carsten Claussen, University of Munich, Albert Schweitzer's understanding of righteousness by faith according to Paul's letter to the Romans Terence L. Donaldson, Wycliffe College, Toronto, The Plain Meaning of Rom. 3:28, 4:5 and the Place of Paul's Juridical Language: A Response to Carsten Claussen Ekkehard Stegemann, Theologischen Fakultat der Universitat Basel, Romans 9-11 in Karl Barth's Doctrine of Election Cristina Grenholm, Karlstad University, Respondent, Romans 9-11 in Karl Barth Alf Christophersen, Ludwig-Maximilians Universitat, Erik Peterson's concept of eschatology William S. Campbell, University of Wales Trinity St. David, Kasemann on Romans: The End of an Era or the Way to the Future? Ian Rock, Codrington College, Respondent Kathy Ehrensperger, University of Wales Trinity St. David, General Respondent, Looking at these Modern Readings of Romans from the New Perspective on Paul Daniel Patte, Vanderbilt University, A Scriptural Critical Look at the Trajectory of Interpretations of Romans since the Enlightenment Biographies Indices.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.012 | 0.067 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".