Job satisfaction: assessing the impact of initiating a professional learning community at Carney Hill Community School
Bibliographic record
Abstract
Working at Carney Hill Community School is emotionally draining and as a result the school struggles with the retention of its staff members. Recognizing this problem, a small group of teachers introduced a new structure that would provide a place for staff to work differently. This different way of working is defined as a professional learning community (PLC). It is a move from working in isolation to working collaboratively in an effort to achieve a shared vision and goals. This new structure would become the vehicle from which the work would be done. As part of that initiative, this research project was designed: to assess whether or not being an active participant of the professional learning community at Carney Hill Community School increased job satisfaction. In order to determine whether or not the PLC increased job satisfaction, a mixed methods approach was used, which included focus groups, individual interviews, and a before and after survey. The intent of the survey was to corroborate the focus group and interview findings. The final analysis suggests that although there was a significant amount of frustration and stress experienced during the initiation of the PLC, it has improved job satisfaction and increased the retention of those staff members and administrators who were active PLC participants.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".