REFLECTIONS ON THE INTERSECTION OF POWER AND COMPETITION IN REFLECTING TEAMS AS APPLIED TO ACADEMIC SETTINGS
Bibliographic record
Abstract
This article explores the author's experiences with the concepts of power and competition as they intersect with aspects of reflecting team practices in academic settings. It draws upon Bishop's (2002) work, the elements of which are based on Starhawk's (1987) ideas of power with and power over. The article describes how competition and other power over practices of domination might unfold and intersect with reflecting-team practices. It is proposed that a power over practice, such as competition, which is prevalent in our society and universities, can be incompatible with the collaborative nature of reflecting teams. Suggestions for addressing the dissonance between the environment and reflecting team practice are offered.
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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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.061 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".