Investigating the making of organizational social responsibility as a polyphony of voices: A ventriloquial analysis of practitioners’ interactions
Bibliographic record
Abstract
Though studies increasingly suggest nurturing a polyphonic and conflict-centered understanding of organizational social responsibility—referred to as CSR here—little is known about which voices make a difference (how and with what effect) when practitioners discuss CSR matters. Similarly, more work is needed on what and how tensions emerge in CSR planning, and how conflicts are addressed. By analyzing conversations with a ventriloquial framework, this research shows that CSR unfolds as different elements of a situation voice themselves as concerns. As the voices of these elements support seemingly incompatible actions, visibility, coherence, and performance tensions surface in interactions. Given that doing CSR consists in responding to concerns and conflicts originating from them, the needs practitioners experience may prompt them to (re)negotiate alternatives for action, balance diverging requests, and/or silence pressing issues to benefit other interests. This study enriches the understanding of CSR as polyphony by unveiling the centrality of voice inclusion–exclusion dynamics in how practitioners try to respond to the (ethical) value of the many conflict- and uncertainty-causing courses of action that manifest in interactions. It also provides insights on the nature of voice mobilization processes, which boost the ventriloquial perspective on organizing. Ultimately, by identifying the making of CSR as unfolding in interplays of voice invitation, mitigation, and shelving, it enhances CSR research by inviting scholars to spotlight more the variability and poly-dimensionality of doing CSR.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".