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Family Engagement in a Pandemic

2022· book-chapter· en· W4294955676 on OpenAlexaffabout
Jacqueline Getfield

Bibliographic record

VenueAdvances in early childhood and K-12 education · 2022
Typebook-chapter
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAlliancePandemicDe factoCoronavirus disease 2019 (COVID-19)LiteracyPedagogyPolitical sciencePublic relationsSociologyMedicineLaw

Abstract

fetched live from OpenAlex

COVID-19 has introduced a new “normal.” During the pandemic, parents of young children and parents of children enrolled in special education have had to assume the role and so perform as teachers. Teachers have had to find ways to include parents who are willing to serve as de facto teachers. In addition to findings from the Ontario Alliance of Black School Educators (ONABSE) webinar series in 2020, this chapter also draws on other publicly available online documents to reveal the ever-changing education landscape in Ontario, Canada during the COVID-19 pandemic. Family literacy was examined broadly through family engagement practices. This chapter shows that there is no one essential story emerging from the pandemic. To date, very few stories have been told of the students who have blossomed and flourished during the pandemic. By gathering stories of the thoughts and actions of Black educators and parents, the chapter augments the existing literature on home-school-community partnerships in Ontario.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.217
Threshold uncertainty score0.432

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0110.005
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.030
GPT teacher head0.322
Teacher spread0.292 · 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 designNot applicable
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

Citations2
Published2022
Admission routes2
Has abstractyes

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