Finding Leisure through Improvisation at Home: Self-Sustainment during COVID-19
Bibliographic record
Abstract
During tumultuous, shaky, and uncertain times such as COVID-19, understandings of and life at home have shifted dramatically, especially amidst lockdown or on-and-off stay-at-home orders. While staying at home made me feel a sense of safety and protection, lockdown was a whole other challenge. Staying at home from morning till dawn, doing the exact same things over and over again, and relying on technology more than ever to fuel my social needs, encompassed my lockdown routine; a routine developed out of desires for normalcy and desperation to feel a sense of stability and so-called productivity. In light of my sanity and survival, I had to make do. I had to improvise. I had to find ‘leisure’ that worked for me while the world moved cautiously amidst COVID. In the details of everyday pandemic life, I found leisure in improvisation. In some cases such as my lockdown experience, leisure can be found in improvisation; not just in one thing or activity per se, but also in a series of pursuits that can help us make do, pass time, keep sane, and even experience temporary bliss and enjoyment amidst an unsteady and unpredictable environment.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
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".