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Quarter Life of Crisis in the Millennial Group in terms of Social Comparison and Resilience

2023· article· en· W4391622055 on OpenAlexaboutno aff
Brigitan Argasiam, Siska Adinda Prabowo Putri

Bibliographic record

VenueANALITIKA · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Resilience (materials science)Psychological resilienceGroup (periodic table)PsychologySocial psychologyHistoryArchaeology

Abstract

fetched live from OpenAlex

Quarterlife of crisis (QLC) is becoming a new trend for individuals who are confused about their role in society. The demands of social roles and expectations from the environment are increasing as individuals enter more complex stages of adult life. The tendency of QLC is experienced by many millennials today, especially in Semarang City. The conduct of this study aims to analyze the relationship between social comparison and resilience to quarterlife of crisis in the millennial group. The sample criteria used were aged 22-29 years and lived in the city of Semarang with a sample of 105 people. The sampling technique used is purposive sampling. The research instruments were quarterlife crisis scale (α = .898), social comparison scale (α = .859), and resilience scale (α = .943). The data analysis method used was regression analysis of two predictors and product moment correlation with JASP 0.16 software. The results of this study showed R = 0.919; R2 = 0.845 and F = 279.002 (p < .0001) means that social comparison and resilience affect the quarterlife of crisis in the millennial group. The value of the coefficient of determination of social comparison variables and resilience to quarterlife crisis variables was 84.5%. The conclusion of the study is that social comparison and resilience simultaneously affect the quarterlife crisis. The implications of the results of this study are expected to be useful not only for millennials but also parents and the surrounding community in understanding their condition about their readiness to face the future by not giving much "pressure" in the form of stigma / negative labels if they do not meet the expected standards.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.069
GPT teacher head0.437
Teacher spread0.368 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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