Bibliographic record
Abstract
Educators and parents alike have high expectations that adolescents behave prosocially and, within the context of schools, this is evident in curriculum grounded in social and emotional learning and in kindness-themed school-wide initiatives. Despite this emphasis on kindness, relatively little is empirically known about how adolescents enact kindness. To understand just how adolescents demonstrate kindness, a study of 191 ninth graders was conducted in which students were asked to plan and complete five kind acts. In addition to planning and doing acts of kindness, participants were asked to rate their face-to-face and online kindness, report the number of kind acts they completed, identify the recipients of their acts, and assess the quality of their kind acts. At post-test, participants’ self-ratings of both face-to-face and online kindness were significantly higher than their pre-test ratings. Only one third of participants completed all of their kind acts, most participants chose familiar others as the recipients of their kindness, and the bulk of participants rated their acts of kindness as medium quality on a low–medium–high scale. The kind acts done by participants reflected the themes of helping with chores, being respectful, complimenting/encouraging others, and giving objects or money. Implications for educators and parents are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".