Love withdrawal use by toddlers: Multi‐informant associations with aggression and parents' use of love withdrawal
Bibliographic record
Abstract
Abstract Love withdrawal is a form of interpersonal manipulation that shares many features with relational aggression; its use by children has not been examined. Guided by social learning theory, we sought to investigate the prevalence of toddlers' use of love withdrawal toward caregivers (parents and teachers) and further investigate how this behavior was associated with relational and physical aggression and parents' use of love withdrawal. These aims were examined using parent and teacher reports in a sample of 198 toddlers (Mage = 33.62 months; SD = 5.00 months; 50.5% girls). We found that most toddlers used love withdrawal directed at parents (79.2%) and teachers (72.1%) when angry and displayed this type of behavior more than relational and physical aggression. Accounting for household income, hours per week in childcare, and child age in months, as well as classroom clustering, we found that relational aggression, and not physical aggression, predicted the use of love withdrawal by toddlers (teacher reports), and that the associations were stronger for love withdrawal and relational aggression than for physical aggression. We also found that parents' use of love withdrawal toward their child was correlated with their reports of their child's use of love withdrawal, but not with their child's use of aggression. These results highlight the importance of considering the use of love withdrawal by children given its association with aggression which is linked to poorer psychosocial functioning.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".