Creating Readers Who Read for Meaning and Love to Read: The Benchmark School Reading Program
Bibliographic record
Abstract
Whole language has become a major movement in literacy education, generating enthusiasm by its proponents and condemnation by its critics. This chapter discusses this issue in detail. First, it argues that traditional researchers have been frustrated in their attempts to define whole language because it is not definable in a conventional sense. Second, it proposes that whole-language programs vary from practitioner to practitioner relative to different construals of intertexts, and that, although its proponents argue that it is, whole language is not a philosophy in the traditional sense. Third, it suggests that whole language is supported by research, but not the comparative research expected by traditional educational researchers, who appear to confuse whole language with related approaches, such as language-experience and meaning-centered instruction. Fourth, it suggests that research involving such approaches as case studies is more appropriate to whole language. Fifth, it proposes that a pragmatic view of research focusing on the results of a program is more relevant to individual teachers than is a fundamentalist view.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".