A revised short version of the compassionate love scale for humanity (CLS-H-SF): evidence from item response theory analyses and validity testing
Bibliographic record
Abstract
BACKGROUND: Compassionate love is defined as awareness and understanding of one's suffering, connecting with the distress, and being emotionally and cognitively moved to alleviate suffering. The Compassionate Love Scale for Humanity (CLS-H) was developed to measure compassion towards strangers who need help and/or are vulnerable. The present study aimed to develop an abbreviated version of the CLS-H using item response theory to provide a precise and non-redundant compassion measure for use in research and practice. METHODS: Undergraduate students (N = 790; 65.8% females) completed the CLS-H and other measures intended to establish external validity. Items for the short version were selected based on high amounts of information and taking into account the content coverage of the construct. RESULTS: The shortened scale consisted of 9 items and performed well in measuring a large spectrum of the underlying construct with acceptable reliability. In terms of validity, the previously observed pattern of correlations was confirmed demonstrating positive associations between compassionate love and measures of self-esteem, positive affect, and life satisfaction, as well as negative associations with negative affect and anxiety. CONCLUSIONS: Using IRT, we obtained a brief, precise, and valid tool for assessing compassionate love.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".