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Record W3085207618 · doi:10.1016/j.heliyon.2020.e04831

Advancements to the Multi-System Model of Resilience: updates from empirical evidence

2020· article· en· W3085207618 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueHeliyon · 2020
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of TorontoToronto Metropolitan UniversityUniversity Health Network
FundersCanadian Psychological AssociationRoyal Bank of CanadaRyerson University
KeywordsResilience (materials science)Vulnerability (computing)Coping (psychology)Computer scienceEmpirical evidenceRisk analysis (engineering)Data sciencePsychologyBusinessComputer securityPhysics

Abstract

fetched live from OpenAlex

In this paper, we discuss further advancements to the Multi-System Model of Resilience through examining empirical factor structures of the Multi-System Model of Resilience Inventory along with other measures of resilience. Evidence from multiple sampled populations provided support for the three-systems organization of the model and highlight its similarities and differences in relation to other measures of resilience. The MSMR conceptualizes resilience as a capacity that enables functioning across a continuum from vulnerability to resilience, whereby internal and external resources interface with dynamic coping processes in response to varying needs and goals. Meaningful applications of this model and future steps in model and measurement developments are discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.196
GPT teacher head0.457
Teacher spread0.261 · 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