The Routinization of Expertise: The Entry of Less-Credentialed Workers into Organizations
Bibliographic record
Abstract
Organizations are increasingly relying on less-credentialed workers to carry out work that has traditionally been performed by experts, such as lawyers, accountants, or physicians. This causes a restructuring of work within the organization, which has implications for experts’ behavior and performance. In this study, we propose that the use of less-credentialed workers prompts experts to focus on performing tasks that differentiate themselves from the other workers. The experts’ increased specialization stimulates the repeated use and provision of these differentiated tasks, even for clients who may benefit from simpler tasks and services. We suggest that this indicates the routinization of their expertise. Furthermore, we expect that the mismatch between experts’ specialization and clients’ needs will reduce worker performance and service quality. Using a difference-in-differences framework, we examine these propositions by exploring the introduction of obstetric nurses in hospitals in Brazil and its impact on the behavior and performance of obstetricians-gynecologists. We find that obstetricians-gynecologists are more likely to perform a caesarian section following the introduction of obstetric nurses in a hospital, resulting in worse care for low-risk births. This study highlights how the use of less-credentialed workers can increase experts’ specialization while reducing experts’ performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".