Let’s Talk About Power: How Teacher Use of Power Shapes Relationships and Learning
Bibliographic record
Abstract
Teachers’ use of power in learning environments affects our students’ experiences, our teaching experiences, and the extent to which learning goals are met. The types of conversations we hold or avoid with students send cues regarding how we use power to develop relationships, influence behaviour and entice motivation. Reliance on prosocial forms of power, such as referent, reward, and expert, have a positive impact on outcomes such as learning and motivation, as well as perceived teacher credibility. Overuse of antisocial forms of power that include legitimate and coercive powers negatively affect these same outcomes. In this paper, we share stories from our teaching experiences that highlight how focusing on referent, reward and expert power bases to connect, problem solve, and negotiate challenges with our students has significantly enhanced our teaching practice. We provide resources that can be used by teachers to become aware of and utilize prosocial power strategies in their practice through self-reflection and peer and student feedback.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".