Bibliographic record
Abstract
Abstract Autonomy support (AS), or autonomy-supportive behavior (AS behavior), is a key ingredient of high-quality hierarchical relationships. Yet how parents and other authority figures can support children’s autonomy—that is, their volitional functioning—in various daily situations remains unclear as AS operationalizations have differed across studies. In an effort to further our understanding of AS behaviors, this chapter highlights their common features (i.e., empathic, informational, and supportive of active participation) as well as their variability. It proposes that AS behaviors may have varied across studies because volition is derived from two different processes (i.e., intrinsic motivation and internalization) and that different AS behaviors may be needed to effectively support volitional functioning originating from each of these processes. Guided by Grusec and Davidov’s domains-of-socialization framework, the chapter argues that intrinsic motivation and internalization are likely to operate differently across domains of socialization, which could account for the variability of AS behaviors. Adopting a domain-specific approach to socialization may thus prove useful to clarify how parents can support their children’s volitional functioning across daily socialization challenges.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".