MétaCan
Menu
Back to cohort
Record W4285789759 · doi:10.51952/9781447350064.ch004

Making spaces in professional learning for democratic literacy education in the early years

2019· book-chapter· en· W4285789759 on OpenAlexaboutno aff
Lori McKee, Rachel Heydon, Elisabeth Davies

Bibliographic record

VenuePolicy Press eBooks · 2019
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsnot available
Fundersnot available
KeywordsDemocracyLiteracyPolitical sciencePedagogyMathematics educationSociologyPsychologyPoliticsLaw

Abstract

fetched live from OpenAlex

Literacy instruction in Canadian classrooms is entangled in neoliberal discourses that can limit teachers’ professional learning opportunities, pedagogical options, and children’s literacy options. And yet, there is hope. This chapter provides illustrations from one first grade classroom that participated in a multiple-case study of professional learning in literacy. The learning was designed to support teachers of children aged 3.8-8 years in creating multimodal literacy pedagogies. Data were collected through ethnographic and narrative methods. Analysis focused on mapping the network that produced classroom change, the children’s responses to the lesson, and the relationship to the professional learning activities. The findings suggest that the professional learning helped to create more connected literacies, joyful engagement in learning, and new relationships between children, teachers, materials, and meaning-making. The findings suggest how democratic literacy education can be fostered through professional learning spaces where teachers can exercise professional discernment and focus on children as pedagogical informants.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.821
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.064
GPT teacher head0.420
Teacher spread0.356 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuePolicy Press eBooksSame topicEducation and Technology IntegrationFrench-language works237,207