Let’s Talk About Power: How Teacher Use of Power Shapes Relationships and Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 it