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Record W7067782132

Normalising standards in educational complexity: A network analysis

2010· book-chapter· en· W7067782132 on OpenAlexaff

Bibliographic record

VenueStirling Online Research Repository (University of Stirling) · 2010
Typebook-chapter
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAuditCompliance (psychology)Key (lock)Competition (biology)Best practiceVariation (astronomy)
DOInot available

Abstract

fetched live from OpenAlex

The proliferation of transnational workplace sites has strengthened the demands for consistent standards of practice and operation. These are increasingly applied and regulated internationally through technologies such as ISO 9000. Workplace learning programs have been designed to reduce variation in skills and procedures at the local level, and to increase individuals’ compliance with regulatory manuals, audit forms, error reports etc. Yet at the same time, a key emphasis for organizations attempting to survive amidst global competition is to increase innovation across different units and different operation levels. This push for innovation has been coupled with ideals of a learning organization wherein all employees are supposed to learn continuously, e.g. to increase variation. This paper explores the organizational tension between centrally imposed demands for both standardized practice and innovative challenges to existing standards that often produces complete separation of design and execution functions, sometimes into sites located in different countries. It shows how in practice, workers continue to experiment and learn in ways that deliberately subvert reductionist standards measures, or that produce local innovations that are unrecognized by these measures.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0400.000

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 head0.047
GPT teacher head0.322
Teacher spread0.276 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2010
Admission routes1
Has abstractyes

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