Understanding healthy administrator-teacher relationships through appreciative inquiry case study
Bibliographic record
Abstract
Through an appreciative leadership lens, the purpose of this case study research was to utilize appreciative inquiry as a methodology to analyze the professional relationships developed and sustained among school administrators and teachers. Additionally, the study sought to understand the associated impacts of healthy administrator-teacher relationships on the school system. This research took place at an alternative high school in a western Canadian city characterized by successful leadership based upon positive relationships between the administrator and the teachers in addition to a positive school culture. The data was collected through narrative free-write, paired interview, focus group, and one-on-one interviews with three teachers and one administrator. The research was guided by the questions: 1) How do teachers and administrators understand the development of symbiotic relationships between administrators and teachers? 2) How do interactions with administrators have personal impacts on teacher bystanders? 3) How do teachers and administrators understand the impact of emotional competence on relationship development? Adding to the current literature, the findings of the research indicate that relationships develop through the supportive practices of the administrator and strong relationships have a positive impact on school culture. Teachers need to be emotionally and professionally supported to develop a positive relationship with the administrator. Teachers feel valued when their opinion is solicited, feedback is given and appreciation is shown. The findings also indicate that emotional competence, which assists individuals in becoming more aware of their emotions and acting accordingly, builds relationships. When administrators and teachers have positive relationships, the school culture is fruitful.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".