Designing resilience research: Using multiple methods to investigate risk exposure, promotive and protective processes, and contextually relevant outcomes for children and youth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Inconsistent, poorly designed research on resilience in the human sciences has contributed to epistemological and ontological ambiguity which has fuelled claims that resilience as a concept is poorly theorized. OBJECTIVE: Building on research with abused and neglected children around the world, the objective of this paper is to show that studies of resilience must account for: (a) risk exposure (of relevance in different contexts); (b) promotive and protective processes (internal and external resources associated with resilience across systems); and (c) desired outcomes (as privileged by stakeholders in different cultures and contexts). METHOD: By identifying common aspects of resilience research from a purposeful selection of studies (ones with weak and strong methodologies), this paper identifies three dimensions of well-designed studies of childhood resilience. RESULTS: Attention to all three dimensions enhances both the empirical validity (in the quantitative research paradigm) and phenomenological trustworthiness (in qualitative research) of resilience research with children and families. Challenges researching resilience can also be resolved by designing studies that account for all three dimensions. These challenges include the lack of systemic thinking to account for contextual factors and other external threats to child wellbeing, and the excessive generalization of findings. CONCLUSION: This three-part model for resilience research reflects the very best practices among resilience researchers and has the potential to address the definitional and methodological ambiguity that plague studies of resilience.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it