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Record W4410981265 · doi:10.18733/cpi29754

Intersubjectivity, Materiality, and Virtuality: What COVID-19 Day-life Taught a Teacher about Navigating a Global Crisis

2025· article· en· W4410981265 on OpenAlexfundvenueno aff
Maria Antonietta Impedovo

Bibliographic record

VenueCultural and Pedagogical Inquiry · 2025
Typearticle
Languageen
FieldPsychology
TopicLeadership, Courage, and Heroism Studies
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsVirtuality (gaming)Materiality (auditing)IntersubjectivityCoronavirus disease 2019 (COVID-19)PsychologySociologyAestheticsArtSocial scienceMedicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This paper explores the intersections of intersubjectivity, materiality, and virtuality through the lens of daily life during the COVID-19 pandemic, focusing on the experiences of a teacher. Using a self-ethnographic approach, it examines how subjective identity intertwines with material and virtual dimensions amid quarantine conditions. The study delves into how professional and personal boundaries blurred, as digital applications, social networks, and online interactions became integral to teaching and everyday activities. It highlights the impact of these changes on our understanding of human-technology relationships, emphasizing the need for new definitions in a technologically mediated society. Through detailed examples, the paper illustrates the complex intersubjective experiences that emerged in a context where the virtual increasingly permeated the material, redefining educational practices and social interactions.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0160.037
Scholarly communication0.0090.007
Open science0.0010.011
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0020.000

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 head0.586
GPT teacher head0.541
Teacher spread0.045 · 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 designQualitative
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
Published2025
Admission routes2
Has abstractyes

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