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Record W4381337833 · doi:10.1080/17482798.2023.2222187

How the COVID-19 shutdown revealed the effectiveness of a northern Nigerian educational media program

2023· article· en· W4381337833 on OpenAlexfundno aff
Dina L. G. Borzekowski, Lauren E. Kauffman, Lauren Jacobs, Mamun Jahun, Hadiza Babayaro

Bibliographic record

VenueJournal of Children and Media · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
FundersGrand Challenges Canada
KeywordsShutdownCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakShut downPandemicPsychologyVirologyEngineeringMedicineNuclear engineering

Abstract

fetched live from OpenAlex

A team of researchers were investigating the impact of a Nigerian adaptation of Akili and Me when the COVID−19 pandemic struck. Schools shut down, interrupting the study’s quasi-experimental intervention design. Post-school reopening, researchers recontacted 363 children (mean age = 5.1, SD = 1.1 years) who had provided data at baseline and had completed the intervention. The analyses revealed that during the shutdown, participating children watched Akili and Me, beyond the exposure experienced through the study intervention. Across viewing groups and including the control group, researchers found the children knew the program’s characters using a program receptivity score. The researchers found no differences associated with study’s initial group assignments. Those children who could name more Akili and Me characters performed significantly better on the outcomes of literacy, numeracy, shape, socio-emotional development, controlling for sex, age, baseline score, and group assignment. This study offers promising evidence that locally-produced educational media interventions can impact early learning skills, especially during a crisis when children rely on educational media for home learning.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.302
Teacher spread0.283 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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