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Record W4403195831 · doi:10.1080/13597566.2024.2410728

Disrupting the national education policy framework: covid-19 measures during the pandemic

2024· article· en· W4403195831 on OpenAlex
Anne Lachance

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueRegional & Federal Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Political Issues
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Political science2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Higher educationEconomic growthPublic administrationRegional scienceGeographyEconomicsVirologyOutbreakMedicine

Abstract

fetched live from OpenAlex

This research note seeks to describe and categorise the responses to the covid-19 pandemic in education in four Canadian provinces between March 2020 and June 2021. It shows that there was a significant variation in the measures that were adopted. Indeed, New Brunswick and Ontario’s social distancing measures in school were much stricter than that of Québec and Alberta. Additionally, Ontario and to a lesser extent Alberta relied on remote learning to a greater degree than the other two provinces. This variation cannot be simply brushed off because of the difference in the number of cases and rather suggests that, in times of crisis, provincial policy responses tend to vary due to framing differences. This research note contributes to federalism literature, exploring the role of subnational governments across an extended period of the pandemic.

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.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.815
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
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 head0.224
GPT teacher head0.517
Teacher spread0.294 · 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