Deficit to Asset Thinking: An Exploration of Incorporating Community Cultural Wealth on Preservice Teachers’ Mindsets
Bibliographic record
Abstract
This qualitative inquiry examined how using Yosso’s community cultural wealth (2005) model as a framework in a 16-week teacher-education course focused on home, schools, and communities which supported the development of 24 preservice teachers’ equity mindsets in relation to these spaces. To examine the nature of preservice teachers’ viewpoints, the following data sources were collected: a researcher-developed survey with open-ended questions based on Yosso’s model used as a pre- and post- survey, reflection assignments, and semi-structured interviews. These data were analyzed using priori coding based on the Equity Mindset Framework (Nadelson et al., 2019). Analysis revealed that Yosso’s community cultural wealth model provided a framework for preservice teachers to develop all eight attributes of an equity mindset to some degree, but three of those attributes were developed at a higher level: development of culturally relevant practices and thinking, the importance of understanding and knowing student populations, and taking responsibility for student success. This research has implications for teacher educators, as it provides guidance for a practical way to enhance preservice teachers’ equity mindset.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".