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Bringing Tasks into the Study of Organizations

2020· article· en· W3046109204 on OpenAlexaffabout
Roman V. Galperin, Lisa E. Cohen, Dylan Boynton, Jillian Chown, Matthew Corritore, Theodore H. DeWitt, Rembrand Koning, Jennifer Merluzzi

Bibliographic record

VenueAcademy of Management Proceedings · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsKellogg's (Canada)McGill University
Fundersnot available
KeywordsTask (project management)SociologyDominance (genetics)Relevance (law)Public relationsWork (physics)ManagementPsychologyPolitical scienceEconomicsEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.123
GPT teacher head0.379
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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