Sticks and Stones: Challenging Oppressive Language In Classroom Settings
Bibliographic record
Abstract
When slogans on t-shirts worn to school receive national attention, the taken-for-granted power of language is accentuated. According to Harmon and Wilson (2006), “it is through language that we learn and come to understand the world we live in, adopt our world-views, become socialized and develop and maintain relationships ” (p. 1). As Johnson and Milani (2010) explain, language plays a role in shaping and maintaining social ideologies, values and practices. This means that language also has the power to validate oppressive social conditions that privilege some groups while marginalizing others. This recognition is extremely important for educators. Acknowledging the deleterious impact of violent and marginalizing language is an important step in the fostering of more inclusive, equitable and socially just educational environments. For educators, this highlights the importance of challenging discriminatory, oppressive and violent language and discourses, even when they surface in informal situations in schools. However, as Jennings (1999) explains, “offering a recipe for success [for how to address oppressive language and discourses] is not easy ” (n.p.); classroom tensions, an educator’s anxieties, and social/institutional responsibilities all influence how these
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.035 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".