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Record W2015665543 · doi:10.5465/ambpp.2012.34

Embedding Disruption: A multi-level model of change in organizational job structures

2012· article· en· W2015665543 on OpenAlexaff
Lisa E. Cohen

Bibliographic record

VenueAcademy of Management Proceedings · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsMcGill University
Fundersnot available
KeywordsOrganizational structureWork (physics)BusinessJob analysisJob designFunction (biology)Job performanceComputer sciencePsychologyJob satisfactionSocial psychologyManagementEconomicsEngineering

Abstract

fetched live from OpenAlex

Though work is changing in response to various global and individual pressures, little is known of how these changes play out at the level of organizational job structures, the very structures into which this work is organized. In particular, what triggers disruption and which jobs within the organizational job structure are most likely to be subject to such disruptive influences? A model is developed that explains the sources for the disruption of organizational job structures and which jobs are most likely to be disrupted. Three dimensions of job structures can be disrupted: the tasks, title, and location. Tasks can be added or removed such that a job is no longer substantively the same. Jobs can be moved horizontally and vertically within organizational hierarchies. Jobs can be altogether eliminated. Which jobs are more likely to be disrupted by these forces is a function of how deeply a job is embedded in the organization, in the unit of the organization, in structures that extend beyond the organization, and in the incumbents holding them.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0030.008
Scholarly communication0.0070.009
Open science0.0030.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0090.002

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 head0.406
GPT teacher head0.446
Teacher spread0.040 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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".

Quick stats

Citations2
Published2012
Admission routes1
Has abstractyes

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