MétaCan
Menu
Back to cohort
Record W4288077534 · doi:10.1177/10860266221108711

Involuntary Disclosures and Stakeholder-Initiated Communication on Social Media

2022· article· en· W4288077534 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOrganization & Environment · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsYork University
FundersNarodowym Centrum NaukiQueen Mary University of London
KeywordsStakeholderCorporate social responsibilityDissentCorporate communicationBusinessPublic relationsSocial mediaStakeholder engagementCrisis communicationAccountingPolitical sciencePoliticsLaw

Abstract

fetched live from OpenAlex

This study explores firm responses to stakeholder-initiated involuntary disclosures, which are disclosures made by stakeholders about an organization but are against the will of managers, and subsequent stakeholder reactions. We analyzed 134,977 firm Twitter replies from seven companies to identify their responses to involuntary corporate social responsibility (CSR) disclosures and find that companies demonstrate different attitudes toward engagement in the exchange about involuntary disclosures. Whereas some companies communicate with stakeholders, others are almost silent. When a company engages in communication with its stakeholders, the communication is mostly one-way, and mortification or dissent is the likely response strategy. We also find that while stakeholders generally do not continue to engage with corporate communications, they are likely to respond when companies deny the information revealed by involuntary disclosure. Our results suggest that involuntary disclosures on social media are not able to improve communication between stakeholders and companies.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.999

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)0.0020.000

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 head0.225
Teacher spread0.158 · 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