What on Earth (or Heaven) Is the “Francis Effect”? A Response to James T. Bretzke, SJ
Bibliographic record
Abstract
James Bretzke notes the ambiguity of the term “Francis Effect” and the difficulty of applying any measures to it. At the root of this difficulty is an ambiguity in the word effect itself. If by this term we mean that some things have transpired as a result of the election of Jorge Maria Bergoglio as the bishop of Rome, then this is trivially true. Had Bergoglio suffered cardiac arrest immediately upon selecting the name Francis (God forbid), even that would have yielded some Francis Effect. Of course, in the media and in Bretzke's essay, the term refers to more than this. For the purposes of this response, I am borrowing three ecclesiastical terms to flesh out possible understandings of this “more”: ordinary, extraordinary, and modal. I take up each of these in turn.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.019 | 0.038 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".