Won’t Stop, Can’t Stop: Alternative Route to Licensure Special Education Teachers’ Persistence in their Careers
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
Alternate route to licensure (AR) programs in special education continue to increase despite concerns that teachers certified through these pathways leave the profession at rates higher than traditionally prepared teachers. The purpose of this study was to examine special education AR program completers to determine their persistence to stay in the profession despite odds of attrition. For this article, we examined survey results from AR special education teachers (n = 57) and completed focus group interviews with a subset (n =13) from this same sample. Using Social Cognitive Theory (SCT) to guide our research, we uncovered three major themes from our focus groups: role conceptualization, barriers experienced, and motivating factors. Our findings suggest that AR special education teachers’ persistence relies on several factors, such as society’s respect for teachers, effective mentoring programs, positive collaboration experience, understanding of their unique role as AR teachers, and self-efficacy. Implications for educational practices, policies, and further research about AR teachers is explored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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