Sponsorship of extracurricular activities and the happiness of educators
Bibliographic record
Abstract
Academic scholarship had not extensively investigated whether sponsoring, coaching, or leading (sponsoring) school based extracurricular activities (ECAs) influenced the sense of happiness experienced by educators. The present author hypothesized that extracurricular activity (ECA) sponsorship would lead to an increase in an educator’s sense of emotional happiness with their school community. The existing thesis’ author proposed the following research question: To what degree, if any, does sponsoring ECAs in an elementary school setting increase an educator’s sense of happiness? Within the current thesis, a thematic literature review examined influences on educator wellness, barriers to sponsoring, and the benefits of extracurricular activities for educators. Hills and Argyle’s (2002) Oxford Happiness Questionnaire (OHQ) provided measures of happiness to test the existing hypothesis. An anonymized, five-member sample, consisting of an unknown combination of teachers, vice principals, and principals (educators) from Nanaimo-Ladysmith School District 68 (SD68), completed the OHQ before, and after, a minimal six-week period of ECA sponsorship. The mean for the existing sample’s OHQ responses statistically verified that sponsoring ECAs had not increased the sample’s sense of happiness. Supplementary statistical data analyzed from the sample’s OHQ responses corroborated that the present study had resulted in a null hypothesis. Research errors made by present author have limited the existing study’s implications and contributions relating to academic scholarship.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".