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Record W4281396698 · doi:10.15273/hpj.v2i1.11370

Finding Leisure through Improvisation at Home: Self-Sustainment during COVID-19

2022· article· en· W4281396698 on OpenAlexaff
Giana Tomas

Bibliographic record

VenueHealthy Populations Journal · 2022
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsImprovisationBLISSSanityCoronavirus disease 2019 (COVID-19)AestheticsEveryday lifePsychologySociologyInternet privacyVisual artsArtMedicineComputer sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.076
GPT teacher head0.365
Teacher spread0.289 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations1
Published2022
Admission routes1
Has abstractyes

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