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Record W1988796205 · doi:10.1287/orsc.1110.0737

Assembling Jobs: A Model of How Tasks Are Bundled Into and Across Jobs

2012· article· en· W1988796205 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOrganization Science · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsProcess (computing)Task (project management)Computer scienceJob designWork (physics)Product (mathematics)Process managementKnowledge managementBusinessJob performanceJob satisfactionManagementSystems engineeringEngineeringEconomics

Abstract

fetched live from OpenAlex

How are tasks bundled into and across jobs within organizations? In this paper, I develop a model of this process of job design by drawing on a multisite qualitative study of task allocation following the installation of a DNA sequencer. The model that emerges is one of the assembly of tasks through multiple subassembly processes with multiple assemblers. Four activities produced requirements and requests for job designs and propositions about how to meet these: actively searching, passively receiving, doing work, and invoking preexisting ideas. The ideas that emerge from these processes are further transformed through reconciliation, interpretation, and performance. My observations show that this overall process is far reaching and incorporates many elements, not all of which are explicitly intended for job designs. The arrangements that emerge from this process are not the product of a deliberate and controlled job design process within the boundaries of a single organization.

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.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0000.001
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 head0.031
GPT teacher head0.259
Teacher spread0.228 · 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