An examination of tension in the space between leadership philosophy and the cultural reality of schools
Bibliographic record
Abstract
Diversity is what gives our society depth and arguably beauty but it also problematizes already complexsocial issues like the importance and value placed on the education. In part, this challenge existsbecause public education is founded on the "values and belief systems of the dominant cultural andlinguistic class" (Goddard & Hart, 2007, p. 16) yet schools are a complex, heterogeneous weave ofcultures (Murakami-Ramalho, 2008). According to Chambers (2003), Canadian students are "probablythe most ethnically, racially, linguistically, and religiously diverse of any school population in the world"(p. 223). This is no less true in the United States where one third of the school population is consideredethnically, linguistically or culturally diverse (Ladson-Billings, 2005). In European countries, the growth ofthe population has also shifted towards greater diversity; Switzerland, for example is now 20% foreignborn (Levin, 2008). Despite this reality, schools leaders struggle to find ways to address the needs ofculturally diverse students and their families (Bazron, Osher & Fleischman, 2005; Goddard & Hart,2007); this challenge creates conflict in schools, particularly for those charged with their 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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".