The COVID-19 pandemic as a tipping point: The precarity of transition for students who receive special education and English language services
Bibliographic record
Abstract
BACKGROUND: School closures and service disruptions related to the COVID-19 pandemic significantly impacted students’ postschool transitions. Students with disabilities who were also members of historically marginalized groups including immigrant students, multilingual students, students of color, and those experiencing poverty, were disproportionately negatively impacted by pandemic-limited services. OBJECTIVE: This paper examined the impact of the pandemic on the transition experiences of secondary students receiving both special education and English learner services. METHOD: We collected and analyzed data from ethnographic interviews with 26 students, their parents, and teachers. A close analysis of a representative case illustrates how transition education and planning were affected by challenges introduced by the COVID-19 pandemic for some of the nation’s most vulnerable students. RESULTS: Despite postsecondary education goals and high parent expectations, evidence of minimal information sharing between school and family, specific plans for goal actualization, and interruptions to service delivery negatively impacted goal attainment, tipping precariously positioned transition plans toward missed opportunities. CONCLUSION: The pandemic accentuated pre-existing inequities in transition and vocational rehabilitation (VR) services. Implications for practice and research are discussed, including the importance of supported family engagement, enhanced self-determination skills, and integrated VR services into high school special education programming.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".