The Moral Relationality of Professionalism Discourses: The Case of Corporate Social Responsibility Practitioners in South Korea
Bibliographic record
Abstract
Building a coherent discourse on professionalism is a challenge for corporate social responsibility (CSR) practitioners, as there is not yet an established knowledge basis for CSR, and CSR is a contested notion that covers a wide variety of issues and moral foundations. Relying on insights from the literature on micro-CSR, new professionalism, and Boltanski and Thévenot’s (1991/2006) economies of worth framework, we examine the discourses of 56 CSR practitioners in South Korea on their claimed professionalism. Our analysis delineates four distinct discourses of CSR professionalism— strategic corporate giving, social innovation, risk management, and sustainability transition—that are derived from a plurality of more or less compatible moral foundations whose partial overlaps and tensions we document in a systematic manner. Our results portray these practitioners as compromise makers who selectively combine morally distant justifications to build their own specific professionalism discourse, with the aim to advance CSR within and across organizations. By uncovering the moral relationality connecting these discourses, our findings show that moral pluralism is a double-edged sword that can not only bolster the justification of CSR professionalism but also threaten collective professionalism at the field level. Overall, our study suggests paying more attention to the moral relationality and tensions that underlie professional fields.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.027 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| 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".