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Record W3199101521 · doi:10.1386/btwo_00045_1

Teaching in the time of COVID

2021· article· en· W3199101521 on OpenAlexaffabout
Lissa Paul, Heather Ferretti, Veronica Lee, Kerry Shoalts

Bibliographic record

VenueBook 2 0 · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsBrock University
Fundersnot available
KeywordsBasketballCoronavirus disease 2019 (COVID-19)CurriculumLimitingRevenuePsychologyMedical educationCompetition (biology)SociologyPedagogyMedicineHistoryEngineeringBusiness

Abstract

fetched live from OpenAlex

This essay arose as a response to teaching the final post-graduate course in the taught master's programme of the Faculty of Education at Brock University (St. Catharine’s Ontario Canada) in the spring and autumn of 2020, just after the onset of the COVID-19 pandemic triggered the complete closures of schools and universities. Three students wrote about their relationships with teaching in the time of COVID. An experienced middle-school teacher discusses how the transition to suddenly homeschooling her five-year-old focused her attention on distinctions between curriculum-driven education and maternal teaching. A newly graduated teacher, concerned about the complete cancellation of extra-curricular sport programmes researches their histories. She discovers the ways in which intercollegiate sport, especially in the United States, transformed what had been healthy competition between undergraduate teams of students into multi-million-dollar businesses driving university revenue streams, eclipsing academic life and exploiting student athletes. In the United States, with academic institutions limiting or prohibiting in-person instruction in 2020-201, basketball and football teams competed. COVID spiked and people died. A nurse-educator, faced with the sudden requirement to remove of all nursing students from their required clinical placements at the onset of the pandemic writes about recalibrating the relationships between virtual experience (including simulations) and practical experience in nursing instruction. Given the vulnerability of clinical placements to sudden closures (SARS in 2003 had been a warning), the nurse-educator explains why it is time to determine which programme components could best be moved online. The contributions by the three students are framed by the professor's own adaptation to an online environment, including her development of asynchronous iMovie instruction combined with short synchronous seminars (with no more than five students at a time) and one-on-one tutorials.

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)0.004
metaresearch head score (Gemma)0.016
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 score0.043
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0280.024
Scholarly communication0.0160.007
Open science0.0020.012
Research integrity0.0050.016
Insufficient payload (model declined to judge)0.0070.002

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 head0.054
GPT teacher head0.420
Teacher spread0.367 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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