Bibliographic record
Abstract
During the pandemic of COVID-19 governments of most countries around the world imposed significant limitations for the freedom of movement and social contacts, which became a source of psycho-emotional issues for some social groups even when there were no noticeable financial consequences of the quarantine. Based on the covid diaries by Russian-speaking scholars in humanities, the article proposes an explanation for the negative reaction of authors to the situation of pandemic from the point of view of neoliberal subjectivity characteristics outlined by Dardot and Laval, within the framework of state of emergency theory by Giorgio Agamben. The research was not focused on socio-economic changes caused by the global quarantine, but on deconstructing the norms and subjectivity features exposed in narratives of covid diaries in response to repressive calls of the pandemic. The discourse analysis method by Potter and Wetherell allows to reveal diaries authors’ attitude regarding the situation and allow to separate it from alternative competing models of reality, while justifying authors’ reaction to what’s going on. There are four interpretative repertoires that are extracted from the discourse analysis of diaries: a repertoire of using space, of managing time, of socialization priorities and of self-care. The analysis shows that the main sources of psycho-emotional issues for the considered social profile were frustration caused by impossibility to implement neoliberal imperatives of high productivity and consumer variety, anxiety caused by the bio-political repressions of the governments and fear of coronavirus.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".