Negotiating Traditions and Transitions: A Response to Elliot R. Wolfson and David F. Ford
Bibliographic record
Abstract
These two papers take us deeply both to the substance and the task of SSR.There is much to praise, and I will offer only brief notes that go forward.Ford's paper begins for us the appropriation of Ochs' work: and the satisfaction for all of us is that the methodology and the logic of Ochs' work has been in practice in SSR (and in other groups)-not the least because Ochs has articulated a methodology for a task which we wanted individually and collectively to pursue.At the center of Ford's appropriation is the question of vagueness, and particularly irremediable vagueness.To appropriate it for us is to begin to think through Scripture as unbound from the `natural' desire to have it say precisely one thing that means precisely one thing.Ford is awake to the theological dimension of vagueness: that it is not merely God's attributes which are vague, but that the working through of their meaning depends on God reserving the authority to re-interpret, or at least to inspire us to re-interpret, the text of Holy Scripture.This reservation of meaning makes the interpretative task itself aware of a Divine intentionality-to refuse a final determination of
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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 | 0.025 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.037 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.030 | 0.035 |
| Insufficient payload (model declined to judge) | 0.005 | 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".