“Immunity‐to‐Change Language Technology”: An Educational Tool for Pastoral Leadership Education
Bibliographic record
Abstract
Abstract. One of the primary aims of pastoral leadership education is to offer reflective processes that enable learners to surface, critique, and construct different epistemological conceptions of reality leading to more effective pastoral practice. In many pastoral leadership education programs, this type of intentional reflection usually takes place in a mentoring or supervisory relationship as well as in a reflective seminar. In this essay, I describe how I have used the “immunity‐to‐change language technology” as one type of reflective process for intentional reflection and transformational learning in pastoral leadership education. The results of my research and ongoing use of this educational tool indicate that it can be valuable for enabling change by helping learners expand their pastoral leadership capacities and become more effective in their practice. Given my findings, I conclude that this educational tool could be of interest to other educators who are seeking to broaden their own repertoire of approaches to transformational learning. A version of this research appears in a forthcoming book by the same author, published by Novalis, in Fall 2008.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".