Between asset and deficit, a proxy to race and existing in the other: pre-service teachers’ perspectives on diversity
Bibliographic record
Abstract
‘Diversity’ is a common word in the vocabulary of institutions of education today. The word tends to be used as a broad term encompassing various social and cultural aspects of one's identity, such as language, religion, race, ethnicity, (dis)ability, sex, and gender, among others. Yet, in teacher education, diversity continues to be used as a label for the Other and for one-dimensional, discrete views of culture and language. One way to engage with diversity critically in teacher education is by employing intersectionality as a framework, which contributes to a more nuanced and comprehensive understanding of diversity. In this article, we draw on a thematic analysis of interviews with ten pre-service teachers to examine their perspectives on and experiences with diversity in teacher education in Norway. Our analysis demonstrates that understandings of diversity tend to evoke more surface-level, one-dimensional representations, stressing discrete identity elements and lacking an intersectional perspective. Furthermore, our analysis illustrates instances in which diversity is seen from a deficit perspective or a problem to be managed and neutralised. We conclude the article with a call for more critical engagements with diversity in teacher education.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.039 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.010 |
| 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".