“But are we going to deal with the hard questions?”: Waves of Compassion in Halifax Regional Municipality
Bibliographic record
Abstract
Within broader social concern about compassion and learning to live well together in the world, a non-profit community-based organization called Waves of Compassion has emerged in Halifax Regional Municipality (HRM) in Nova Scotia, Canada. In this article, we explore how compassion relates to some “hard questions” that have arise for the organization—questions related to issues of marginalization and inclusivity: for example, what it might mean to “walk in another’s shoes,” particularly when that person or group of people is different from you in terms of age, race, ethnicity, sexual orientation, socioeconomic status, or citizenship. We also wonder what role the Waves organization might take up in terms of action and/or practice with regard to transforming inequity and promoting inclusivity in the community. We consider such questions in the context of data derived from a recent survey that Waves of Compassion undertook. We integrate found poems (many of which are derived from the survey data) and expository writing as means of underlining what some writers have said about compassion—that it involves both emotions and rational thought, the undoing of sharp distinctions between the two. We see compassion as a form of practice where boundaries and separations might be dissolved (at least at times) through being and knowing in different ways.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".