Sing A New Song! Theological Reflection Based in Hymn Text Writing: A Workshop
Bibliographic record
Abstract
Scripture exhorts the faithful to “Sing a new song!” but for many worshipers their songs are still predominantly those of a bygone theological era in terms of the language and imagery with which both God and people are described. Since hymns have long been understood to be major contributors to the shaping of personal theologies, and with inclusivity a contemporary justice issue in the United Church of Canada, it seemed desirable to find a way to address this perceived problem. \n\tBased on the commonly accepted premise that one learns best by doing, and supported by the dialogic adult learning strategies developed by educators Jane Vella and Patricia Cranton, a workshop in hymn-text writing for parishioners was designed and tested with ten volunteer participants. The purpose of this intervention was two-fold: to evaluate the structure and teaching efficacy of the workshop itself while teaching participants the basics of hymn construction, style and content, and to offer participants an opportunity for theological reflection and discovery. An open-ended, process theology approach was the underlying orientation; the researcher was interested to learn what types of transformative thinking or behaviours the participants might experience as a result of their hymn text writing and small-group discussions. \n\tThe workshop was deemed a success, in that every participant produced a thoughtful, technically sound hymn and there was general agreement that it had been a positive experience. Weaknesses in the workshop design were identified and will be amended in future editions. Some type of transformative learning took place in each individual, with a range from primarily factual information to deeply spiritual insights. The long-term impact of the experience remains to be seen, but if only a general sense of empowerment through the new skill of hymn writing, and an increased awareness of hymns and their role in faith expression are the immediate results of the exercise, it was worthwhile. \n\tAlthough a few other hymn-text writing workshops have been published, this one breaks new ground by consciously adding the element of theological reflection. With some adaptation, it can be used by denominations outside the United Church.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".