Patterns and Possibilities: Exploring the Meaning of Kindness
Bibliographic record
Abstract
Abstract In this chapter, authors take a narrative/interpretive approach by sharing insights from millennials and Generation Z as to the definition of kindness as a behaviour and action. Sixty-six individuals living in North America, Africa and Europe were interviewed during the pandemic (October 2020). They were asked to describe an incident in which they expressed kindness and/or in which it was expressed to them. Authors identified five themes (metapatterns) which denote different ways kindness is described through narrative. These kindness behaviours include: (1) kindness as a small act, (2) kindness as an event, (3) kindness as intervention and (4) kindness as consideration. The fifth form of kindness operates with more performative qualities, and the authors' have dubbed it as ‘kindness [that] makes me feel good’. Authors attempt not to constrain or essentialize what kindness behaviour is, but rather to reveal patterns while also leaving room for possibilities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".