Annual Research Review: What is resilience within the social ecology of human development?
Bibliographic record
Abstract
BACKGROUND: The development of Bronfenbrenner's bio-social-ecological systems model of human development parallels advances made to the theory of resilience that progressively moved from a more individual (micro) focus on traits to a multisystemic understanding of person-environment reciprocal processes. METHODS: This review uses Bronfenbrenner's model and Ungar's social-ecological interpretation of four decades of research on resilience to discuss the results of a purposeful selection of studies of resilience that have been done in different contexts and cultures. RESULTS: An ecological model of resilience can, and indeed has been shown to help researchers of resilience to conceptualize the child's social and physical ecologies, from caregivers to neighbourhoods, that account for both proximal and distal factors that predict successful development under adversity. Three principles emerged from this review that inform a bio-social-ecological interpretation of resilience: equifinality (there are many proximal processes that can lead to many different, but equally viable, expressions of human development associated with well-being); differential impact (the nature of the risks children face, their perceptions of the resources available to mitigate those risks and the quality of the resources that are accessible make proximal processes more or less influential to children's development); and contextual and cultural moderation (different contexts and cultures provide access to different processes associated with resilience as it is defined locally). CONCLUSION: As this review shows, using this multisystemic social-ecological theory of resilience can inform a deeper understanding of the processes that contribute to positive development under stress. It can also offer practitioners and policy makers a broader perspective on principles for the design and implementation of effective interventions.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".