Identity work of corporate social responsibility consultants: Managing discursively the tensions between profit and social responsibility
Bibliographic record
Abstract
Critical evaluations of the current movement of corporate social responsibility (CSR) commodification have neglected an important question: How do CSR professionals manage the tensions resulting from the search for both profit and social responsibility? This article addresses this question by analyzing the discourse of CSR consultants with the aim of understanding how they deal with such tensions through identity work. Our findings suggest that people who claim, or who are ascribed, paradoxical professional identities may engage in ‘paradoxical identity mitigation’ – a process whereby the concomitant and paradoxical use of linguistic strategies is aimed at simultaneously embracing and distancing oneself from contradictory identity demands. In uncovering how CSR professionals discursively manage the tensions engendered by CSR commodification, our results also advance current knowledge of CSR by shedding light on the underlying processes whereby new ‘hybrid’ identities are constituted and mobilized by actors to make sense of their professional activities.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.046 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".