MétaCan
Menu
Back to cohort

How are Today’s Workers Mobilizing to Address Social and Environmental Challenges?

2023· article· en· W4385191812 on OpenAlexaff
Hadi Shaheen, Jean‐Baptiste Litrico

Bibliographic record

VenueAcademy of Management Proceedings · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsPublic relationsContext (archaeology)InsiderShareholderSocial movementSoftware deploymentWork (physics)Resistance (ecology)Political scienceBusinessCorporate governancePoliticsLaw

Abstract

fetched live from OpenAlex

This study takes the context of bottom-up activism and explores the ways that today’s workers are mobilizing to address societal challenges. It comprises a qualitative comparative research design using process study to identify the practices through which internal activists attempt to influence their firms. Drawing from social movement literature, the work explores recent employee activists’ campaigns’ use of tactics, informational content in their claims, and tools to drive two organizations to address societal challenges. We identify two new insider activism tactics: shareholder resolutions and resignations. Findings also suggest that tactics change over time. Facing unmet demands, activists shift from institutionalized mechanisms of change to confrontational and disruptive tactics. Informational content in their confrontational claims reveals that workers expose internal knowledge about their firm’s market and non-market activities. We also identify two demand escalation strategies: horizontal and vertical, that can explain outcome variations. Theoretically, this suggests that employee activists today have more complex repertoires than initially thought. The deployment of these repertoires changes over time as activists are met with resistance from the firm. We also find that organizations may offer concessions but still sanction employees, suggesting a more nuanced view of organizational response to internal activism.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.770

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.067
GPT teacher head0.263
Teacher spread0.197 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAcademy of Management ProceedingsSame topicRegulation and Compliance StudiesFrench-language works237,207