Co-creating Knowledge and Shaping Practice: The collaborative work of nascent occupations
Bibliographic record
Abstract
Organizations increasingly create specialized roles to address complex challenges that transcend established boundaries of expertise and practice. This study examines how members of a nascent occupation—responsible investment analysts—collaborated with asset management professionals to incorporate environmental, social, and governance criteria into conventional financial analyses. Drawing on ethnographic fieldwork and multiple data sources, we identify two distinct collaborative pathways: structured collaboration, which aligns nascent occupational contributions with existing frameworks, and generative collaboration, which involves sustained, iterative engagement to fundamentally transform established practices. We show that progression along each pathway is shaped by the interplay between nascent occupation members’ adaptive strategies and the epistemic assumptions embedded in professional domains—particularly shared beliefs about what constitutes valid expertise and how it should be evaluated. Our findings illuminate how knowledge co-creation unfolds when one party lacks institutionally validated expertise, how professional-level epistemic orientations shape opportunities for practice transformation, and how early collaborative arrangements condition nascent occupations’ ability to gain recognition and influence within organizations. These insights contribute to theories of knowledge co-creation across occupational boundaries and offer practical guidance for organizations seeking to address challenges that span established domains of expertise.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| 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".