Does A Family Size Have an Effect on Empathy Level? : Filling the Gap in Empathy Literature
Bibliographic record
Abstract
The purpose of this study was to scrutinize the effect of a number of family members on empathy level. A sample of 67 students in 2-year program in accounting who registered in strategic management course was gathered for data collection. An adjusted version of the Toronto Empathy Questionnaire was employed to assess empathy level among students. Findings indicated that the average number of students’ family members was 4-5 people. The empathy level of students was reported at a high level (M = 2.85, S.D. = .345). Analysis of linear regression analysis designated that a number of family members could explain 7.1% of variance to empathy level among this group of students. Results also found that a number of family members significantly predicted students’ empathy level (s = .266, p <.05). In conclusion, the more family members a student had, the more empathetic he/she was
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".