Practicing truth, in silence: reflecting on the use of non-disclosure agreements in North American ecclesial contexts
Bibliographic record
Abstract
With the necessary advent of #MeToo and #ChurchToo, there has been growing debate regarding the use of non-disclosure agreements in ecclesial contexts. While the contemporary use of non-disclosure agreements (NDA) stifle truth-telling and are energized by the threat of penalty, churches throughout North America (and elsewhere) have not just continued to use these clauses but have increased their use. Is it possible to utilize NDA clauses in a way that glorifies God? In this paper, I reflect on the use of non-disclosure agreements, reviewing key literature to describe the complex history and use of these clauses. I offer a theological reflection on the character of God and Scripture’s instruction regarding lawsuits and truth-telling in order to complexify the church’s practice of utilizing NDA clauses and invite the reader to reflect on whether Christians should continue to entertain the use of these clauses given the negative practical and theological implications found in their use.
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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 | 0.030 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.041 | 0.060 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".