Self‐Esteem as a Complex Dynamic System: Intrinsic and Extrinsic Microlevel Dynamics
Bibliographic record
Abstract
The variability of self‐esteem is an important characteristic of self‐esteem. However, little is known about the mechanisms that underlie it. The goal of the current study was to empirically explore these underlying mechanisms. It is commonly assumed that state self‐esteem (the fleeting experience of the self) is a response to the immediate social context. Drawing from a complex dynamic systems perspective, the self‐organizing self‐esteem model asserts that this responsivity is not passive or stimulus‐response like, but that the impact of the social context on state self‐esteem is intimately connected to the intrinsic dynamics of self‐esteem. The model suggests that intrinsic dynamics are the result of higher‐order self‐esteem attractors that can constrain state self‐esteem variability. The current study tests this model, and more specifically, the prediction that state self‐esteem variability is less influenced by changes in the immediate context if relatively strong, as opposed to weak, self‐esteem attractors underlie intrinsic dynamics of self‐esteem. To test this, parent‐adolescent dyads (N = 13, Mage = 13.6) were filmed during seminaturalistic discussions. Observable components of adolescent state self‐esteem were coded in real time, as well as real‐time parental autonomy‐support and relatedness. Kohonen’s self‐organizing maps were used to derive attractor‐like patterns: repeated higher‐order patterns of adolescents’ self‐esteem components. State space grids were used to assess how much adolescents’ self‐esteem attractors constrained their state self‐esteem variability. We found varying levels of attractor strength in our sample. In accordance with our prediction, we found that state self‐esteem was less sensitive to changes in parental support and relatedness for adolescents with stronger self‐esteem attractors. Discussion revolves around the implications of our findings for the ontology of self‐esteem.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".