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Emerging Insights on Social Class at Micro and Macro Levels

2024· article· en· W4400444903 on OpenAlexaffabout
Steven Kardel, Jennifer J. Kish-Gephart, Sharvika Kherde, Andrea Dittmann, Kristin Laurin, Muhan Zhang, Michelle K. Lee, Shelby Gai, Sridhar Polineni, Caren Colaco, Stéphane Côté

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsUniversity of TorontoQueen's University
Fundersnot available
KeywordsMacroClass (philosophy)Data scienceComputer scienceArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

This symposium highlights emerging insights related to social class, an important and overlooked aspect of diversity in organizations. The five papers presented in this symposium will cover various topics within the micro and macro domains of management. At the micro level, the papers reveal the complex interplay of social class background with other marginalized identities, such as gender and immigrant status, in shaping workplace experiences. Their nuanced analysis underscores the need to consider social class as a part of multiple identities shaping the lives of students and employees. Additionally, this symposium also integrates social class and conflict literature, proposing that class-based self-conceptions underlie the responses to workplace conflict. At the macro level, the papers delve into how CEO’s social class background shapes stakeholder perceptions and career outcomes. They enrich our understanding of how class-based stereotypes and shifting competence standards follow executives to the apex of corporate hierarchy, carrying career implications even after they have demonstrated merit through significant upward mobility. Together, these papers significantly advance our understanding of how social class, though often invisible, has a profound impact on attitudes and outcomes in the workplace. Roots and Rungs: Exploring Immigrant Realities of Class and Culture Intersectionality Author: Sharvika Kherde; - Author: Andrea Dittmann; U. of Southern California - Marshall School of Business Exploring the Impact of Gender and Social Class Background on Status Inconsistency & Employee Voice Author: Sridhar Polineni; Ross School of Business, U. of Michigan Social Class and the Experience of Conflict at Work Author: Caren Colaco; - Author: Kristin Laurin; U. of British Columbia Author: Stephane Côté; U. of Toronto Not So Undercover Bosses: CEO Social Class Background and Employee Approval Author: Steven Kardel; Pennsylvania State U. Author: Muhan Zhang; The Chinese U. of Hong Kong, HK The Role of Social Class Background on the Relationship between Performance & CEO Career Outcomes Author: Michelle K. Lee; Smith School of Business, Queen's U. Author: Shelby Gai; Michigan State U.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.440

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.090
GPT teacher head0.380
Teacher spread0.290 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2024
Admission routes2
Has abstractyes

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