Motivation Profiles of Urban Preservice Teachers: Relations to Antecedents, Outcomes, and Demographics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Given the perennial challenge of attracting and retaining high-quality teachers, especially in large cities, there is a need to understand why preservice teachers in urban districts choose a teaching career, their perceptions of the profession, and how these relate to their initial career commitments and aspirations. Using latent profile analysis, we examined patterns of motivational perceptions with variables from the Factors Influencing Teacher Choice model alongside perceived task effort cost, opportunity cost, and emotional cost of teaching within a diverse sample of 630 preservice teachers. We identified four distinct profiles that differentially related to theorized antecedents (prior teaching and learning experiences, social encouragement, fallback career) and outcomes (satisfaction, planned persistence, planned professional development, leadership aspirations). Race, gender and certification-level were distributed in unique patterns across profiles. Results provide a holistic perspective of preservice teacher motivations and indicate that perceived costs in relation to FIT Choice variables were a defining characteristic of motivational patterns.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it