The Contributions of Temperament Characteristics to Attachment in 6-Year-Old Children
Bibliographic record
Abstract
The aim of the current study is to investigate whether 6-year-old children’s temperament predict their level of attachment. The study has a descriptive design. During the Spring term of the academic year 2017-2018, 59 children (60-72-month old) and their mothers in City of Tekirdağ (Süleymanpaşa District) volunteered to participate in the study. Demographics Form, Incomplete Stories with Doll Family and Short Temperament Scale for Children were used for the data collection. Descriptive statistics were performed to summarize the ISDF and STSC scores in the first step of the data analysis. A multiple linear regression was carried out and attachment scores and temperament sub-dimension scores were assigned as the dependent and independent variables, respectively. Multiple linear regression analyses revealed that the independent variables in the model predicted 16% of the dependent variable (R2 = .157), which meant that the children’s temperament predicted 16% of their attachment levels. Reactivity, sociability, rhythmicity and the linear combination of children’s reactivity, persistence, sociability and rhythmicity did not predict children’s attachment, but persistence did.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".