Which Hill Would You Die on?: Examining the Use of War-Normalizing Metaphors in Social Justice Leaders’ Discourse and Practice
Bibliographic record
Abstract
Metaphors are deeply embedded in educational discourse, yet few studies examine how educators use these linguistic devices to conceptualize, articulate, and make sense of their professional practice. This article examines the metaphors that 38 Canadian and American school leaders used to describe how they accomplished their social justice work in complex political environments. Our analysis revealed that while participants used a variety of metaphors to describe how they subverted inequitable practices to achieve their social justice goals, for the most part, their discourse coalesced around war-normalizing metaphors. We explore the nature of these metaphors, how they contradict and cohere with popular educational discourses and ideologies, and their implications for practice. We further discuss how policy makers, practitioners, and professional development programs can employ metaphors as discursive tools to assess and reconceptualize practice and advance social justice leadership.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.047 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".