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The Plight of the Vulnerable Workforce: Theoretical and Empirical Advancements

2023· article· en· W4385220737 on OpenAlexaff
Catherine Deen, Simon Lloyd D. Restubog, Daniel S. Samosh, Janice Lam, Brent J. Lyons, Yueyang Chen, Anna Carmella Ocampo, Lu Wang, Anthony Decoste, Yaqing He, Ryan D. Duffy, Constantin Lagios, Patricia Tabarani, J. Ryan Lamare

Bibliographic record

VenueAcademy of Management Proceedings · 2023
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsScholarshipWorkforceSociologyGlobeCriminologyPolitical sciencePublic relationsPsychologyLaw

Abstract

fetched live from OpenAlex

The work experience of the vulnerable workforce is a relevant yet generally understudied phenomena in the management literature. Global development agencies such as the United Nations (UN) and the International Labour Organization (ILO) consistently emphasize the importance of decent work not only as a human rights issue but also as an essential component of a sustainable society. Thus, it is undeniable that academic scholarship involving vulnerable workers is necessary and timely. Advocating theory-driven scholarship, this symposium offers compelling studies that applied a variety of theoretical perspectives to examine the plight of vulnerable workers (e.g., people living with HIV, victims of intimate partner aggression, mental illness, ethnic minorities). We offer four papers anchored on strong theoretical foundations (e.g., self-determination theory, identity formation theory, stereotype-content model, etc.) that enrich our understanding of the vocational experiences of vulnerable workers. These studies also consist of a diverse range of research designs (e.g., experiment, mixed-methods, archival, longitudinal/time-lagged). Moreover, with studies completed from different countries (e.g., USA, Philippines, GLOBE), this symposium offers an opportunity for discussion about cross-national differences and perspectives. This symposium sets the stage for more theory-driven scholarship, thereby contributing to building a stronger body of work on the vulnerable workforce. Observer Responses to Job Candidate Disclosure of Bipolar Disorder Author: Daniel S. Samosh; Queen's U. Author: Janice Lam; Schulich School of Business, York U. Author: Brent John Lyons; Schulich School of Business, York U. The Impact of HIV Stigma at Work: A Self-Determination Perspective Author: Yueyang Chen; U. of Illinois at Urbana-Champaign Author: Anna Carmella Ocampo; ESADE Business School Author: Simon Lloyd D. Restubog; U. of Illinois at Urbana-Champaign Author: Lu Wang; Australian National U. Author: Anthony Decoste; Global Virtuoso, Inc Work Consequences of Intimate Partner Aggression: A Self-Determination Perspective Author: Yaqing He; U. of Illinois at Urbana-Champaign Author: Catherine Deen; U. of New South Wales Author: Constantin Lagios; Catholic U. of Louvain Cross-cultural Analysis of Voice Behaviors Across Three Marginalized Groups Author: Patricia Tabarani; U. of Illinois at Urbana-Champaign Author: J. Ryan Lamare; U. of Illinois at Urbana-Champaign

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)0.020
metaresearch head score (Gemma)0.033
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: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0090.011
Science and technology studies0.0060.021
Scholarly communication0.0120.012
Open science0.0030.006
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.054
GPT teacher head0.420
Teacher spread0.366 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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