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Record W4323032188 · doi:10.1177/02557614231157101

The effects of the pandemic on music teaching in schools in Quebec (Canada) in the spring and fall of 2020

2023· article· en· W4323032188 on OpenAlexaffabout
Audrey‐Kristel Barbeau, Hélène Boucher, Isabelle Héroux

Bibliographic record

VenueInternational Journal of Music Education · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsSolidarityMusic educationPsychologyPedagogyPandemicCoronavirus disease 2019 (COVID-19)Mathematics educationOnline teachingMusicalSociologyMedical educationVisual artsPolitical scienceMedicine

Abstract

fetched live from OpenAlex

COVID-19 containment measures brought many changes in our lives and forced teachers all around the world to adopt various new practices. Given its specific education requirements and numerous school boards, the province of Quebec, Canada, was chosen to study the effects of the pandemic on music teaching in schools in the spring and fall of 2020. An electronic survey was distributed, to which 517 elementary and high school music teachers responded. Teachers reported on the transformation of teaching modes from an exclusively in-person practice to an online or bimodal approach. Continuation and interruption of music programs varied greatly from school to school and, for those who were allowed musical activities, different protective health measures were implemented. Teachers working with large ensembles (e.g. band and orchestra) experienced more interruptions in their music programs. Teachers also reported how their planning was affected by the new modes of instruction, but no matter which modes were used, most of them experienced less motivation for teaching during the spring of 2020. In addition, they perceived that it was more motivating for students to receive an education in person. Finally, positive outcomes of the pandemic on education included the development of new skills in the use of digital resources and online teaching, as well as a renewed sense of solidarity between teachers.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.738
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.024
GPT teacher head0.257
Teacher spread0.232 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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