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When and How Are Allies Promoters of Social Change? An Examination of Allyship in the Workplace

2022· article· en· W4286668342 on OpenAlexaboutno aff
Terrance L. Boyd, McKenzie Preston, Denise Lewin Loyd, Rachel Arnett, Tianna Shari' Barnes, Richard Burgess, Stephanie J. Creary, Juanita Kimiyo Forrester, Tiffany Dawn Johnson, Karren Knowlton, Angelica Leigh, Vic Marsh, Ozias Moore, Natasha Reed, Roxanne Ross, Enrica N. Ruggs, Horatio Traylor

Bibliographic record

VenueAcademy of Management Proceedings · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipLegislaturePolitical sciencePublic relationsPoliticsState (computer science)SociologyCollective actionMedia studiesLaw

Abstract

fetched live from OpenAlex

Allies have played instrumental roles in efforts to address inequity in society. In the wake of civil unrest triggered by the shooting deaths of unarmed Black people, the brutalization of Asian Americans during the COVID-19 pandemic, and state legislatures passing laws that seek to reduce voting and women’s rights, the supportive action taken by allies is gaining attention once again. This year’s Academy of Management Conference theme “Creating a Better World Together” is a call to action for scholarship that provides insight into how organizations can anticipate and solve these important societal challenges. Yet, despite management scholars suggesting allyship is an effective way to create organizational change, the literature on allyship is still quite scarce. This symposium includes a collection of papers that examines open research questions pertaining to allyship including: who or what entities are able to engage in allyship behavior, why those actors choose allyship, and when (i.e., which contexts) support allyship. Following the presentations, Denise Lewin Loyd, a major contributor to research in the field of diversity and inclusion, will serve as our symposium’s discussant. Together, we hope that this symposium will provide important insights into how to create meaningful change in organizations through allyship. LEAP at Work: Examining the Effects of Race-Based Allyship Training in the Workplace Presenter: Stephanie J. Creary; The Wharton School, U. of Pennsylvania Presenter: Tianna Shari' Barnes; U. of Pennsylvania Presenter: Ozias Moore; Lehigh U. Institutional Allyship, a Ritual of Recovery Presenter: Tiffany Dawn Johnson; Georgia Institute of Technology Presenter: Juanita Kimiyo Forrester; Mercer U. Presenter: Natasha Denise Reed; Georgia Institute of Technology Bursting the Bubble of Performative Allyship: How Moral Performance Compromises Inter-Group Learning Presenter: Karren Kimberly Knowlton; Tuck School of Business at Dartmouth Presenter: Rachel Arnett; The Wharton School, U. of Pennsylvania Who’s Expected to be an Ally? An Examination of Allyship and Leadership Evaluations Presenter: McKenzie Preston; The Wharton School, U. of Pennsylvania Presenter: Angelica Leigh; Fuqua School of Business, Duke U. Presenter: Terrance L. Boyd; Louisiana State U. Presenter: Richard Burgess; U. of North Carolina, Chapel Hill Presenter: Vic Marsh; U. of Toronto, Rotman School of Management Practice What You Preach: Performative Allyship in Organizations’ Support for Racial Equity Presenter: Roxanne Ross; James Madison U. Presenter: Horatio Traylor; U. of Houston Presenter: Enrica Nicole Ruggs; U. of Houston

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)0.008
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.009
Scholarly communication0.0080.007
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.039
GPT teacher head0.233
Teacher spread0.194 · 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 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
Published2022
Admission routes1
Has abstractyes

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