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Record W4390298691 · doi:10.1080/13676261.2023.2299270

Individual, failing: an analysis of film portrayals of the causes of young adult coresidence from 2010–2020

2023· article· en· W4390298691 on OpenAlexaffabout
Brenan R. R. Smith, Kathrina Mazurik, Jan Gelech

Bibliographic record

VenueJournal of Youth Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsStorytellingPsychologyYoung adultDevelopmental psychologyArtNarrative

Abstract

fetched live from OpenAlex

Although researchers have observed that film depictions of young adult coresiders (i.e. young adults who live with their parents) tend to be negative, no research has systematically analyzed or explored such portrayals. Adapting Qualitative Content Analysis, this study examined portrayals of Canadian and American young adult/parent coresidence in films released between 2010 and 2020, seeking to ascertain the explanations for coresidence, (i.e. how do films portray why young adults coreside). Analysis of 18 films yielded eight distinct forms of explanations for coresidence, with the two most common being those that rendered coresidence as occurring due to either a mental health challenge or the flawed personality of the coresider. Broadly, film portrayals depicted coresidence as symptomatic of an individualized failing of young adults. Interpreting these findings through the lens of psychocentrism, we argue that film constructions pathologize coresidence and responsiblize coresiders. Further, and in contrast to news media, film constructions ignore systemic or structural drivers of coresidence. This research is the first to examine film portrayals of coresidence in depth, highlights distinct depictions of young adults in film media, and draws attention to the discrepancies between documented reasons for coresiding and those portrayed in on-screen storytelling.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.147
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.062
GPT teacher head0.344
Teacher spread0.283 · 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.

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

Citations1
Published2023
Admission routes2
Has abstractyes

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