Complexity theory and implications for teacher learning research.
Bibliographic record
Abstract
Primary grade teachers are positioned with the enormous responsibility of having to provide their students foundational academic and social skills. Students who do not learn to become proficient readers by the end of Grade 3 show higher incidences of high school drop-out, incarceration and teen pregnancy than average to good readers (Torgesen, 2000; Vanderstaay, 2006). Contemporary 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 literacy instruction. This paper shows how a complexivist guided research design sensitive to the contextual relationships inherent between teachers, their schools and school boards is conducive for understanding teachers’ early reading professional learning needs. Applying an instrumental qualitative case study research design involving three schools in an Eastern Canadian public school board with in-depth interviews with early reading teachers, principals and board reading specialists (N=12), this paper presents findings of complexivist guided research and discusses how complexivist research is ideal for drawing out deep, highly personal perspectives that should contribute to our understanding of how the uniqueness of context and underlying interrelated factors are influencing teacher professional learning, early reading instruction and student reading achievement.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".