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Record W2100867682 · doi:10.1177/154193120004401272

The Conditions for Apprenticeship in a Work Situation: Margins of Maneuver and Resources to be Changed by the Individual, the Group and the Organization

2000· article· en· W2100867682 on OpenAlexaff
Céline Chatigny

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2000
Typearticle
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsApprenticeshipMargin (machine learning)Work (physics)Knowledge managementFocus (optics)Human resourcesBusinessOperations managementComputer scienceEngineeringEconomicsManagementMachine learningMechanical engineering

Abstract

fetched live from OpenAlex

An ergonomic analysis of the learning conditions and strategies in two types of occupations demonstrates the existence of various degrees of use and construction of organizational, human and material resources. These strategies are individual and collective, and focus on an increase in the margin of maneuver for doing and learning how to do the work in an efficient way. They play a major role but are developed and implemented under conditions that often make them costly because they are limited by the margin of maneuver imposed by the companies.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.202
Teacher spread0.188 · 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 designQualitative
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
Published2000
Admission routes1
Has abstractyes

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