“Spiritual Metrics as a Bulwark Against Secularization”
Bibliographic record
Abstract
Almost a year and a half ago, on May 4, 2023, the Accord Research Alliance (ARA) community of practice of the Accord Network organized a virtual symposium to discuss David Bronkema’s “Spiritual Metrics as a Bulwark Against Secularization: Reflections on Jayakumar Christian’s Words at the ARA on “Slicing Off” the Spiritual.” The purpose of the symposium, and of Bronkema’s thought piece that underpinned it (and published as part of this issue of the journal), was to tackle the critique advanced by Jayakumar Christian in the ARA pre-conference intensive of October 2022 on the danger of engaging in spiritual metrics in a way that separated out the spiritual from everything else. The symposium was moderated by Peter Howard of the Accord Network. The first part consisted of introductory remarks by David Bronkema (a professor at Eastern University) and responses to Bronkema’s paper by Jayakumar Christian (former National Director, World Vision India), James Waters (founder and director Kingdom Impact, Ltd.), Katie Toop (senior director of transformational development at World Concern), Subodh Kumar (vice president of mission impact for Food for the Hungry), and Victor Cortez (regional director for Latin America at Water Mission). This was followed by a discussion in which David Bronkema and Jayakumar Christian responded to the comments that had been proffered, after which the respondents were given the opportunity a to add their own observations to the discussion. Below is an edited version of the remarks at the symposium. The full recording of the symposium can be found at https://vimeo.com/823841424.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.048 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.004 | 0.015 |
| 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".