Intersection of the self and the system: Early reading professional learning
Bibliographic record
Abstract
Research findings on student reading achievement and early reading teacher professional learning and knowledge present a juxtaposed picture of the current state of the field of early reading instruction: (1) compared to proficiently reading peers students struggling to read by grade 3 are at a higher risks of high school dropout, teen pregnancy and incarceration; (2) too many students read at basic and below basic levels by grade 4; (3) many early reading teachers lack adequate knowledge of core early reading skills; and (4) many early reading professional learning programs do no help teachers meet their struggling readers’ needs. Such findings provided the impetus for the qualitative case study described in this paper. Inquiry into teacher, principal and board reading specialist perspectives (N=12) on the contextual variables influencing the planning, delivery and uptake of early reading professional learning are represented in four emergent themes: teacher engagement, teacher self-interest, teacher receptivity, and teacher vulnerability to system flux. This research opens up the nature of the relationships between teachers and the contextual variables influencing their teacher and learning opportunities. Findings provide new points from which educational researchers, schools and school boards can consider more contextually relevant early reading professional learning designs.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".