Creating our class story: a narrative inquiry into a mainstream grade one teacher's journey with EAL students
Bibliographic record
Abstract
Diverse cultures continue to intersect on the school landscape in Manitoba. This autobiographical narrative inquiry (Clandinin & Connelly, 2000) documents 4 years of my experience as a Grade 1 elementary teacher working with an increasing number of English as an Additional Language (EAL) students. Between 2008 and 2011, I invited each class of students to engage in a collaborative writing project entitled Our Class Story where they shared their personal journeys to Grade 1. Drawing upon my field notes, journal entries, previous writings, and photographs, I tell four separate stories of my experience as this class project evolved over time with different groups of students. I then explore how working on Our Class Story shifted my teaching practice and beliefs about teaching EAL Early Years Learners.
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.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.018 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".