MétaCan
Menu
Back to cohort
Record W2131899124 · doi:10.1111/1469-7610.00631

Psychopathology and Short‐term Emotion: The Balance of Affects

2000· article· en· W2131899124 on OpenAlexaff
Jennifer M. Jenkins, Keith Oatley

Bibliographic record

VenueJournal of Child Psychology and Psychiatry · 2000
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychopathologyPsychologyBalance (ability)Term (time)Developmental psychologyClinical psychologyCognitive psychologyNeuroscience

Abstract

fetched live from OpenAlex

In this study, the relationship between short-term emotion expressions and dimensional ratings of internalizing and externalizing symptomatology was examined. Short-term emotions, defined as facial or vocal displays of emotion generally lasting less than 10 seconds and elicited by a specific and proximal event, were observed during recess in 71 children from diverse socioeconomic backgrounds, who were between 4 and 8 years old. Internalizing and externalizing symptomatology was assessed through parent and teacher questionnaire. Sociometric ratings were obtained from peers on children's anger and aggression. It was hypothesized following Tomkins (1979) and others that one affect becomes predominant in the emotional experience of the individual. Different operationalizations of this concept were examined. Using regression analyses, externalizing symptomatology was found to be predicted by higher levels of anger, lower levels of happiness, and lower levels of sadness. Internalizing symptomatology was found to be predicted by higher levels of sadness and lower levels of anger. It was concluded on the basis of these data that the relationship between short-term emotion and internalizing and externalizing psychopathology is best understood as the balance between different short-term emotions. Results are discussed in the context of theories of emotion and their functions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.282
Teacher spread0.269 · 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 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

Citations32
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Child Psychology and PsychiatrySame topicMental Health and PsychiatryFrench-language works237,207