Bringing Tasks into the Study of Organizations
Bibliographic record
Abstract
Organizations exist as locations for the execution of work, and tasks are the micro units of that work. Tasks are also at the core of jobs, occupations, and careers, and shape the experiences of employees in organizations, jobs, and occupations. Their creation, movement, destruction, and accumulation determines the shape and arguably the critical outcomes for these social structures which in turn are at the heart of critical social processes. Yet the focus on tasks is lacking in the organization theory research. This symposium aims to demonstrate the breadth of insights that can be gained from focusing on task-level organization of work. The four papers comprising the symposium demonstrate the importance of accounting for tasks when studying professional work, innovation, and inequality. Jurisdictional Relevance, Jurisdictional Dominance, and the Diffusion of New Professional Tasks Presenter: Dylan Boynton; Northwestern Kellogg School of Management Presenter: Jillian Chown; Northwestern Kellogg School of Management Mapping the Task Structure of Jobs with Natural Language Processing Presenter: Theodore DeWitt; U. of Massachusetts Boston Skill Bundling and Inequality: How Job Role Skill Breadth Relates to Occupational Wage Returns Presenter: Matthew Corritore; McGill U. - Desautels Faculty of Management Presenter: Rembrand Michael Koning; Harvard Business School Homophily in Advice Networks and the Task-Level Organization of Work Presenter: Roman V. Galperin; McGill U. - Desautels Faculty of Management Presenter: Jennifer M. Merluzzi; George Washington U.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| 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".