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Record W4225263755 · doi:10.3917/rips1.072.0005

Les contributions mutuelles entre les ONG locales et les entreprises : un facteur de crédibilité du rapportage social fondé sur trois piliers

2022· article· fr· W4225263755 on OpenAlexaff
Olivier Braun, Agnès Ceccarelli, Christine Morin-Estèves

Bibliographic record

VenueRevue internationale de psychosociologie et de gestion des comportements organisationnels · 2022
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsMinistère de l’Emploi et de la Solidarité Sociale (Québec)
Fundersnot available
KeywordsHumanitiesPolitical scienceSociologyPhilosophy

Abstract

fetched live from OpenAlex

Les critiques portant sur la qualité des informations extra financières divulguées aux parties prenantes, y compris aux O.N.G, sont constantes. Le manque de crédibilité du rapportage social revêt autant des enjeux financiers que des enjeux d’opérationnalisation du développement durable. La collaboration des O.N.G à l’échelle territoriale, locale, avec les entreprises est peu abordée dans les recherches empiriques de même que leur contribution mutuelle à la crédibilisation du rapportage social. Notre recherche qualitative réunit des experts du développement durable et des représentants des O.N.G locales dans un espace de dialogue dont les confrontations font ressortir des réflexions qui aideront les entreprises à améliorer la qualité de leur rapportage social. Nous montrons du point de vue managérial que la crédibilité s’appuie d’une part sur une collaboration nourrie entreprises/O.N.G locales selon trois dimensions : une posture collaborative, des moyens et des objectifs communs puis des valeurs partagées. D’autre part, nous montrons que les O.N.G contribuent par leurs actions à la construction du contenu du rapportage social. Notre apport académique concerne la crédibilité du rapportage social qui se cristallise sur trois piliers : son instrumentalisation, sa construction et la divulgation des informations.

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)0.006
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient 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 score0.078
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.077
GPT teacher head0.314
Teacher spread0.237 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueRevue internationale de psychosociologie et de gestion des comportements organisationnelsSame topicCorporate Social Responsibility ReportingFrench-language works237,207