Intersubjectivity, Materiality, and Virtuality: What COVID-19 Day-life Taught a Teacher about Navigating a Global Crisis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.037 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".