Making it count: a narrative inquiry into one teacher's experiences supporting middle school EAL students
Bibliographic record
Abstract
This autobiographical narrative inquiry explores the teaching, learning and leadership experiences of a middle school teacher in Manitoba. My early experiences as a classroom teacher reflect my uncertainty and unpreparedness of a teacher who struggled to meet the needs of the English as an Additional Language (EAL) students who entered my classroom. As the EAL student population increased within my middle school, I began the journey of a Masters program to seek knowledge in order to support my EAL students and to help guide my colleagues towards an inclusive environment. As I explored how my experiences as a graduate student had influenced my classroom practices, and then how my experiences as an EAL specialist and school leader had influenced the school community, five main themes emerged: The use of the iPad in a mainstream classroom, the use of effective instructional strategies, the role of culture in the classroom, co-teaching practices and collaboration in a Middle School setting. Through narrative inquiry I investigated these themes and discovered new pathways to support EAL students and guide my colleagues while moving toward a more inclusive classroom and school environment.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.020 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| 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".