Appendix C Data and Interview Questions for RCWP Communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data and Interview Questions for RCWP Communities Although womenpriests are the central focus of this book, RCWP's ordained women do not exist in isolation.The following offers some basic information about the people who regularly attend Mass and are active in RCWP-led communities.Most of the following information comes from three studies: a 2014 electronic survey I conducted through the RCWP and ARCWP listservs; a 2011 MA thesis for Drew University's Theological School ("Waiting for Wisdom: Sophia's Response to the Roman Catholic Church's Position on Priesthood"), written by Allison Delcalzo; and an undergraduate thesis in women and gender studies from Washington University in St. Louis ("All Are Welcome: The Roman Catholic Women's Ordination Movement and the Motivations of Participants"), written by Caitlyn Gaskell. 1 I have also developed a strong sense of RCWP congregants through news stories, documentary interviews, and participant observation.Data combined with ethnographic research revealed distinctive patterns within North American RCWP communities (no parishioners outside of the US and Canada opted to take my survey), especially when compared to recent trends in American Catholic demographics (as reported primarily by the Pew Research Center).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it