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Record W2905705336 · doi:10.4324/9780203052891-10

Creating Readers Who Read for Meaning and Love to Read: The Benchmark School Reading Program

2013· book-chapter· en· W2905705336 on OpenAlexaff

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsReading (process)Meaning (existential)Benchmark (surveying)Computer scienceLinguisticsPsychologyLiteratureMathematics educationArtPhilosophyCartographyPsychotherapist

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.703
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.043
GPT teacher head0.348
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations6
Published2013
Admission routes1
Has abstractyes

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