MétaCan
Menu
← Back to cohort
Record W4236406897 · doi:10.32920/ryerson.14647221

Resilience as governmentality : a narrative study within mental health.

2021· preprint· en· W4236406897 on OpenAlexaff
Philip Mach

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGovernmentalityResilience (materials science)NarrativeMental healthPsychologyMental illnessPsychological resilienceOrder (exchange)Social psychologyPolitical sciencePsychiatryBusiness

Abstract

fetched live from OpenAlex

The study of resilience in individuals is typically conducted to enumerate the ways in which a person may be resilient to difficult life circumstances. Resilience, in this way, is seen as a means to overcoming adversity in order to succeed in life. In the current study, the concept of resilience is examined in terms of the way it may guide or limit the actions of individuals, as governmentality. Specifically, this study focuses on the lives of survivors of mental illness and ascertains the ways in which resilience affects how they have chosen, or have been taught, to manage their mental illness.

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)0.005
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.013
Scholarly communication0.0060.008
Open science0.0010.006
Research integrity0.0020.005
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 head0.041
GPT teacher head0.451
Teacher spread0.410 · 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 designQualitative
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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicResilience and Mental Health→French-language works237,207→