Motivation Profiles of Urban Preservice Teachers: Relations to Antecedents, Outcomes, and Demographics
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".