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Record W2106124558 · doi:10.1177/0044118x03257030

A Constructionist Discourse on Resilience

2004· article· en· W2106124558 on OpenAlexaff
Michael Ungar

Bibliographic record

VenueYouth & Society · 2004
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsDalhousie University
Fundersnot available
KeywordsStrict constructionismCausality (physics)Construct (python library)SociologySocial constructionismPerspective (graphical)Resilience (materials science)Psychological interventionInterpretation (philosophy)PsychologyPsychological resilienceNarrativeSocial psychologyEpistemologySocial scienceComputer science

Abstract

fetched live from OpenAlex

An ecological approach to the study of resilience, informed by Systems Theory and emphasizing predictable relationships between risk and protective factors, circular causality, and transactional processes, is inadequate to account for the diversity of people’s experiences of resilience. In contrast, a constructionist interpretation of resilience reflects a postmodern understanding of the construct that better accounts for cultural and contextual differences in how resilience is expressed by individuals, families, and communities. Research supporting this approach has demonstrated a nonsystemic, nonhierarchical relationship between risk and protective factors that is characteristically chaotic, complex, relative, and contextual. This article critically reviews research findings that support an ecological perspective and explores the emerging literature that informs a constructionist approach to the study of resilience. It will show that an alternate constructionist discourse on resilience greatly enhances our understanding of resilience-related phenomena and our approach to interventions with at-risk youth populations.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0120.092
Scholarly communication0.0080.014
Open science0.0020.010
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.026
GPT teacher head0.384
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations638
Published2004
Admission routes1
Has abstractyes

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