“I Don’t See Color”: The Impact of Field Placements on Preservice Teachers’ White Racial Identity Development
Bibliographic record
Abstract
The purpose of this mixed-method study was to examine the influence of diverse field placements on the Whiteracial identity development of White preservice teachers (n = 92) placed in schools where the student body waseither predominantly White or students of color. Using Helms’s theory (1995) of White racial identitydevelopment, we selected instruments that measured participants’ awareness of racism, as well as theirconsciousness about being White (e.g., Color-Blind Racial Attitude Scale and Psychosocial Costs of Racism toWhites Scale). Preservice teachers in nondiverse settings became less aware of racial issues at the end of thefield experience. Using pretest scores as covariates, an analysis of covariance indicated that those in morediverse settings had higher levels of White guilt at the end of their field experience. The qualitative results alsoshowed differences in perceptions based on field placements, thus supporting the quantitative findings.Participants were asked how the diversity in their fieldwork placement affected their thoughts about their ownethnic background and social status. For those placed in diverse settings, the most common theme that emergedwas the contrast between the characteristics of the students and one’s own family and personal characteristics(e.g., wealth, ethnicity). The results suggest that more than exposure to diverse students is needed to evokechanges in White racial identity in order to prepare preservice teachers to effectively teach students of color.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".