On Compassion and Community Without Identity: Implications for Moral Education
Bibliographic record
Abstract
When one thinks of community, what often comes to mind is people who have something in common -common interests, ideas, or ideals, or perhaps a shared identity based on geography, culture, language, race, or religion.Community is also often accompanied by somewhat romantic notions of a kind of togetherness against the ravages of the world outside.As Zygmunt Bauman puts it, Words have meanings: some words, however, also have a "feel."The word "community" is one of them….To start with, community is a "warm" place, a cosy and comfortable place.It is like a roof under which we shelter in heavy rain, like a fireplace at which we warm our hands on a frosty day.Out there, in the street, all sorts of dangers lie in ambush; we have to be alert when we go out, watch whom we are talking to and who talks to us, be on the lookout every minute.In here, in the community, we can relax…..We may quarrel, but these are friendly quarrels, it is just that we are all trying to make our togetherness even better and more enjoyable than it has been so far and, while guided by the same wish to improve our life together, we may disagree how to do it best.But we never wish each other bad luck, and we may be sure that all the others around us wish us good. 1
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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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.115 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.011 | 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".