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Record W2499474008 · doi:10.1177/1948550616660590

Accuracy and Positivity in Adolescent Perceptions of Parent Behavior

2016· article· en· W2499474008 on OpenAlexaff
Lauren J. Human, Meanne Chan, Rafa Ifthikhar, Deanna Williams, Anita DeLongis, Edith Chen

Bibliographic record

VenueSocial Psychological and Personality Science · 2016
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaMcGill University
Fundersnot available
KeywordsPsychologyPerceptionDevelopmental psychologyContext (archaeology)Social perceptionDepression (economics)Clinical psychology

Abstract

fetched live from OpenAlex

Forming accurate perceptions is often linked to positive relationship and individual functioning, yet may also be detrimental in some contexts. The current study examined whether accuracy may be detrimental to individual functioning, both psychological and physiological, in an important social context: parent–adolescent relationships. Specifically, we examined whether the accuracy of adolescents’ perceptions of their parent’s behaviors was associated with adolescent psychological adjustment (depression and perceived stress; N dyads = 99) and proinflammatory profiles ( N dyads = 95). Adolescents who viewed their parent’s behaviors more accurately (more in line with external observers’ ratings) reported worse psychological adjustment and demonstrated worse regulation of the inflammatory response. In contrast, adolescents who viewed their parent’s behaviors highly normatively and positively reported better psychological adjustment. Overall, these findings suggest that adolescent accuracy regarding parent behaviors may be detrimental to adolescent psychological adjustment and inflammatory processes.

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)0.016
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 score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.147
GPT teacher head0.424
Teacher spread0.277 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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