Pandemic digital structural violence: Teachers' observation of post‐pandemic learning loss in students
Bibliographic record
Abstract
Abstract Almost four academic years have passed since emergency remote teaching (ERT) was employed as a temporary means for continuing education. In the post‐pandemic era, residual impacts from ERT are still unfolding. Teachers reported a pronounced decrease in students' academic performance, concentration and social skills. As time passes, we seem to have forgotten the negative impacts of ERT on students, which affects primary, secondary, and even university students. Using case studies, digital ethnography and autoethnography, this research explores ERT in a private school in Canada and a local Band 3 school in Hong Kong. The qualitative data allow an extensive analysis of the circumstances and outcomes of two diverse groups of students. The findings include class participation as an outcome of limited resources; students' motivation and independent learning skills differ on the basis of their socioeconomic status; and the issues of mind wandering and concentration, which manifest in various ways. Despite school resumption, these findings show that the negative impacts remain in today's classrooms. This research argues that the negative consequences differentially affect students and proposes the need to coin the term ‘pandemic digital structural violence’ (PDSV) to address the core problem accurately. This research urges educators to be aware of PDSV and avoid blaming their students. It also urges policy makers to address the unfairness while moving on to develop digitised education further.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".