Effective whole-language teaching : case studies of two teachers' practice
Bibliographic record
Abstract
Whole-language theory, as an approach to language arts instruction, has been the subject of a wide and varied literature that has attempted to define, describe, validate and understand it. This research project is concerned with the issue of "effective whole-language teaching" as demonstrated by case study descriptions of two teachers' practice of whole-language. Using ethnographic techniques for data collection, each teacher's practice has been documented and analyzed in terms of themes that have emerged from the data. The analysis contained within each identified theme contains a descriptive and critical account of the kinds of "effective teaching" skills/strategies that have been identified in each classroom. A final discussion is offered that attempts to draw conclusions about the research question, making some recommendations about effective whole-language teaching. It is expected that these will contribute to a body of knowledge that addresses specific methods and strategies that may be used by teachers interested in whole-language education.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".