Innovation in experts' work: Adoption and diffusion of tasks communities of expert practice
Bibliographic record
Abstract
In this paper, we explore the dynamics of expert learning by examining experts’ adoption of new tasks. We apply a theory of absorptive capacity to expert communities of practice, focusing on how the existing body of expert work is an important antecedent for future patterns of expert learning. We theorize that two dimensions of expert work – the complexity and the breadth of knowledge required – are particularly important aspects of the absorptive capacity of experts, and hypothesize that communities that engage in more complex work and draw on more domains of knowledge will search for, and incorporate, new tasks faster and more thoroughly. Furthermore, we hypothesize that the alignment between the new tasks and the experts’ existing work is an important predictor of adoption. Using a unique dataset of tasks performed by over 20,000 physicians over 10 years, we find support for the importance of work complexity, but not knowledge breadth, in shaping in the likelihood and speed of adoption. Interestingly, we find that high complexity and broad knowledge can limit how far new tasks diffuse within a community. Discussion focuses on the benefits and future possibilities of studying work as a site of innovation and considering absorptive capacity at the level of work and practice.
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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 | 0.015 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".