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Record W4296598173 · doi:10.1177/10664807221123554

Too Much of a Good Thing? Associations among Parenting Profiles and Helicopter Parenting

2022· article· en· W4296598173 on OpenAlexaff
Karina A. Turner, Sara R. Elkins, Christine A. P. Walther, Mary B. Short, G. Thomas Schanding

Bibliographic record

VenueThe Family Journal · 2022
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsParenting stylesPsychologyDevelopmental psychologyConstruct (python library)Latent class modelSet (abstract data type)Positive parentingChild rearingSocial psychologyIntervention (counseling)

Abstract

fetched live from OpenAlex

Helicopter parenting is defined by popular culture as a set of parenting practices characterized by overinvolvement. However, empirical research has not adequately defined the construct, and it is unclear how helicopter parenting may relate to existing parenting behaviors and parenting styles. Participants included 341 caregivers from across the United States who completed a survey about their parenting beliefs and behaviors. Latent class analysis was used to determine combinations of parenting behaviors, and the identified classes of parents were then used to conduct one-way ANOVAs to examine differences in helicopter parenting between the different classes. Results suggest that parents who fit the authoritative parenting profile endorse greater helicopter parenting. Further research needs to be conducted to determine the “tipping point” for when positive parenting practices, which are often associated with positive outcomes, begin to produce negative outcomes.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.028
GPT teacher head0.276
Teacher spread0.248 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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