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Record W6992360434

Language and literacy workshops: supporting the learning of four focal English language arts practices through the use of quality texts

2016· dissertation· en· W6992360434 on OpenAlexaffabout

Bibliographic record

VenueMspace (University of Manitoba) · 2016
Typedissertation
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsNucleofectionDysgeusiaPretextGestational periodHyporeflexiaLiquationDurvalumab
DOInot available

Abstract

fetched live from OpenAlex

In Manitoba, a new English Language Arts (ELA) curriculum focusing on language and literacy practices invites learners to authentically and meaningfully engage with a variety of texts in the classroom. This thesis supports educators by valuing their professional judgment, as they are provided with researched text selection criteria and called upon to evaluate and choose texts of rich quality for use with children in classrooms in the beginning years of school (Kindergarten–Grade 2). Drawing upon this ELA curriculum, the author questions, provides insight, and reflects on how a variety of multimodal texts could be incorporated into the classroom learning by interweaving the four key literacy and language practices that represent valued ways of thinking, being, and doing in ELA. The author’s insights are presented in a written workshop format, in which a critical literacy stance is adopted in order to examine, discuss, and analyze an assortment of multimodal texts.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0030.001
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.056
GPT teacher head0.300
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations0
Published2016
Admission routes2
Has abstractyes

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