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Confronting Descriptive-Prescriptive Paradoxes in the Pursuit of Equity, Justice, and Knowledge

2025· article· en· W4415999901 on OpenAlexaffabout
Ilana Brody, Stephanie J. Creary, Jacob W. Roberson, Grusha Agarwal, Linnea Gandhi

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsContext (archaeology)Diversity (politics)Status quoPsychological interventionRepresentation (politics)

Abstract

fetched live from OpenAlex

Management scholars can advance theoretical insight with significant practical implications by identifying when and why status quo procedures are failing to satisfy intended goals, and develop theoretically- informed solutions. The scholars in this symposium document descriptive- prescriptive paradoxes when confronting systemic barriers to equity, justice, and knowledge accumulation. In doing so, the speakers not only contribute novel theoretical insights that advance the field’s understanding of individual and system-level goal pursuit, but also demonstrate clear practical implications for individuals and organizations to better align their actions with their goals. Striving to survive or to thrive? Motives for aid seeking in the context of SNAP Author: Ilana Brody; University of California Los Angeles Author: Sherry Jueyu Wu; University of California Los Angeles Author: Eugene M. Caruso; University of California Los Angeles Author: Heather M. Caruso; University of California Los Angeles Advancing DEI or imposing diversity work? The pernicious effects of misguided representation goals Author: Jacob W. Roberson; USC Marshall School of Business, University Of Southern California Author: Leigh Plunkett Tost; University of Southern California Spoken softly, heard less: The gendered use of passive voice in workplace complaints Author: Grusha Agarwal; University of Toronto Author: Sonia Kang; University of Toronto An empirical test for how well the literature on behavioral interventions generalizes Author: Linnea Gandhi; The Wharton School, University of Pennsylvania Author: Elizabeth Tipton; Northwestern University Author: Duncan J. Watts; University of Pennsylvania

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.826
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.138
GPT teacher head0.371
Teacher spread0.234 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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