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Record W2121474628 · doi:10.1109/iemc.2005.1559119

Businesses, universities and engineering management

2005· article· en· W2121474628 on OpenAlexfundno aff
J. Bishton, Neil Allan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
FundersLancaster UniversityMcGill University
KeywordsEngineering managementComputer scienceBusinessEngineering

Abstract

fetched live from OpenAlex

This discussion paper takes a look at the changing requirements of businesses and at the capabilities of university management schools, in order to address the question: why does industry constantly complain about the shortage of managerial talent? There is no doubting the talent of professional engineers, but their contribution to business management is not good enough. The needs of businesses are investigated and it is suggested that three levels of management development should be visualised. However, companies tend to send selected staff on university management programmes as an act of faith, without becoming involved in programme design or delivery. This detachment is not discouraged by universities and reasons are explored briefly, but it is noted that 'management' cannot be regarded simply as a science or as an intellectual discipline. Programme delivery is visualised in four stages, with companies needing at least three and universities only able to deliver two at best. It is recommended, therefore, that programme design and delivery be regarded as an integrated activity, involving companies and universities. A revolutionary shift in working practices will be required. The experiences of the Engineering Management Partnership (EMP) in the UK are outlined, showing that the present programme, which meets many of the needs of industry, should be extended to include company-specific application.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.002
GPT teacher head0.157
Teacher spread0.155 · 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".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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