Toward a Servant-Led Response Rooted in Forgiveness and Restorative Justice in the Catholic Clergy Sexual Abuse Scandal
Bibliographic record
Abstract
A STORY OF CLERGY ABUSE AND FORGIVENESS ASKING A chill in the air, fog smearing his windows, Tom Blanchette drove west of Boston to Lexington. He had an appointment with Father Joseph Birmingham. It had been twenty-five years since Birmingham's going-away party in Sudbury, where they last spoke. "How could I ever explain to anybody that I had laid naked in Father B.'s bed over a hundred times? I didn't know how to explain these experiences. All I knew was I didn't like it. But I saw him every Saturday and Sunday, because I worked in the church rectory; I saw him at least one night a week for catechism classes, and at least one night a week he was at our house for dinner. Every time we were alone he pursued sexual activity. For two years I would say we had sex two or three times a week, sometimes two or three times a day." Until he was in his twenties, Tom had never mentioned a word of this history. One morning as Tom and an old friend were reminiscing about their teenage years, Father Birmingham's name came up. The friend grew angry and said, "that bastard ... he queered me." Tom then polled his brothers and learned that Father B. had solicited sex from them too. In a span of a week, Tom and his mother compiled a list of twenty-five victims.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.058 | 0.039 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.011 | 0.028 |
| Insufficient payload (model declined to judge) | 0.008 | 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".