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Record W4309315529 · doi:10.3390/su142215098

In the Eye of the Hurricane: Careers under Lockdown

2022· article· en· W4309315529 on OpenAlexaff
Maria Mouratidou, Mirit K. Grabarski

Bibliographic record

VenueSustainability · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsLakehead University
FundersUniversity of Cumbria
KeywordsPsychologyPerceptionEmployabilityEmpowermentOptimismApplied psychologyPsychological resilienceSocial psychologyTest (biology)Political sciencePedagogy

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has created career disruptions and shocks for many individuals, due to layoffs, reduced work hours and increased work–life conflict. Our study aimed to explore individual-level perceptions of people regarding their careers during the first lockdown in the UK, and to test potential implications of the situation for individuals’ career sustainability. For a deeper understanding of these perceptions, we used a sequential mixed-methods research design. First, we conducted a qualitative study, using semi-structured interviews to explore how people perceive their careers during early stages of the pandemic. We identified two themes that characterize the common experiences during this time period, namely employer support and careful optimism, that play an important role in the way careers unfold. Then, in the quantitative study, we conducted an online survey to empirically test a research model that links the concept of employer support with employability, career satisfaction and mental well-being. We also investigate the mediating role of career empowerment, which is a motivational cognitive construct that captures individual cognitions of agentic control over one’s career. Our research provides a rich snapshot that depicts people’s perceptions of careers during a shock event, which has both theoretical and practical implications.

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 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)0.003
metaresearch head score (Gemma)0.001
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 score0.178
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.348
Teacher spread0.333 · 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 teacher head, not a consensus.

Study designObservational
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

Citations5
Published2022
Admission routes1
Has abstractyes

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