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Record W2162315440 · doi:10.1177/0190272514546698

Control in the Face of Uncertainty

2014· article· en· W2162315440 on OpenAlexaffabout
Paul Glavin, Scott Schieman

Bibliographic record

VenueSocial Psychology Quarterly · 2014
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of TorontoMcMaster University
Fundersnot available
KeywordsDistressControl (management)Job lossSocial psychologyAssociation (psychology)PsychologyMental healthPerceived controlDemographic economicsClinical psychologyEconomicsUnemploymentPsychiatryEconomic growth

Abstract

fetched live from OpenAlex

The mental health benefits of the sense of personal control are well documented, but do these benefits persist in social contexts of powerlessness and uncertainty? Drawing from two national panel surveys of American and Canadian workers, we examine whether the association between perceived control and reduced distress is undermined by the uncertainty of threatened employment. While we find evidence that higher levels of perceived control are associated with reduced distress, the association is curvilinear among insecure workers, such that subsequent increases in control produce diminishing reductions in distress for workers reporting the threat of job loss. This curvilinear pattern is particularly prominent among American insecure workers, with higher than moderate levels of control associated with more rather than less distress for this group. We draw from Mirowsky and Ross’s “instrumental realism” model to interpret these patterns and suggest that high control beliefs may be less beneficial for mental health in uncertain role contexts.

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)0.002
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.006
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.051
GPT teacher head0.452
Teacher spread0.402 · 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

Citations22
Published2014
Admission routes2
Has abstractyes

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