Bibliographic record
Abstract
How do tasks and expertise coevolve? Tasks and expertise are tightly linked and essential for the execution of work in organizations. Despite the link in the practice of tasks and expertise, scholars have yet to theorize them as coevolving. A small body of research provides evidence that by failing to treat them this way, research misses insights critical to explaining the evolving nature and future of work. To explore these dynamics, we analyze interviews and observations of people involved with the data-collection task in an early-stage startup. We trace the task’s detailed movement across jobs and by doing so, observe complex dynamics between the task and associated expertise. The task moved from proto-analysts to analysts to data-entry operators and, with that, the core expertise required for the task moved across positions. In addition, doing data collection produced expertise that those in the analyst position applied in performing other tasks. In part, because it facilitated the production of expertise, analysts continued to collect data – a task that they routinely complained about doing – even after it migrated to data-entry operators. Based on these findings, we develop the distinction between core and produced expertise. We also refine our understanding of hiving-off with an alternative explanation for why menial tasks might not be hived-off. Finally, our findings enhance our understanding of the dynamics around change in jobs and task segregation.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".