Rethinking professionalization: A generative dialogue on CSR practitioners1
Bibliographic record
Abstract
Abstract Studies of emerging professions are more and more at the crossroad of different fields of research, and field boundaries thus hamper the development of a full-fledged conversation. In an attempt to bridge these boundaries, this article offers a ‘generative dialogue’ about the redefinition of the professionalization project through the case of corporate social responsibility (CSR) practitioners. We bring together prominent scholars from two distinct academic communities—CSR and the professions—to shed light on some of the unsolved questions and dilemmas around contemporary professionalization through an example of an emerging profession. Key learnings from this dialogue point us toward the rethinking of processes of professionalization, in particular the role of expertise, the unifying force of common normative goals, and collaborative practises between networks of stakeholders. As such, we expand the research agenda for scholars of the professions and of CSR.
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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 | 0.090 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.040 | 0.139 |
| Scholarly communication | 0.028 | 0.030 |
| Open science | 0.005 | 0.033 |
| Research integrity | 0.015 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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".