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Record W4220915291 · doi:10.15273/jue.v12i1.11315

The Social Life of High School Seniors: COVID Experiences

2022· article· en· W4220915291 on OpenAlexvenueno aff
Clara Maxwell, Thomas Van Rijckevorsel

Bibliographic record

VenueJournal for Undergraduate Ethnography · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsSocial isolationCoronavirus disease 2019 (COVID-19)PandemicContext (archaeology)PsychologyIsolation (microbiology)Mental healthQualitative researchSocial environmentGovernment (linguistics)Social psychologyDevelopmental psychologySociologyMedicineSocial sciencePsychiatryDisease

Abstract

fetched live from OpenAlex

COVID-19 marks a time of social isolation and social change in the lives of many people. While previous literature has focused on the mental health consequences of isolation on young people, our qualitative research aims to explore the lived experiences of adolescents during the pandemic. Based on 10 in-depth, semi-structured interviews with senior year students at the John F. Kennedy School in Berlin, this study seeks to determine whether both the government and school-imposed COVID-measures have impacted the social lives of our participants and to understand how they experience these potential changes. Our research found that students report a significant change in social life, but, in contrast with the existing literature, their experience of this social change is perceived as positive. These positive changes included a reported improvement in social connections, a more conscious use of social media, and the potential for more alone time. By exploring these three themes, our participants’ unexpected positivity can be placed into a larger context in which the pandemic is an opportunity to forge more meaningful connections while learning to be more conscious in spending time alone in an “always-on” culture.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.997

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 studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.078
GPT teacher head0.427
Teacher spread0.349 · 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 designNot applicable
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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