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Record W3126322249 · doi:10.1680/jmapl.20.00051

Productivity and industrial relations in the Australian construction industry

2021· article· en· W3126322249 on OpenAlexaboutno aff
Martin Loosemore, Suhair Alkilani, Santiago Luperdi

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Management Procurement and Law · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityConstruction industryBusinessLegislationIndustrial relationsIndustrial organizationWork (physics)EngineeringEconomicsPolitical scienceManagementEconomic growthConstruction engineeringLaw

Abstract

fetched live from OpenAlex

The global construction industry has a poor productivity record compared with other industries. While there have been many studies into the factors that influence construction productivity, the role of industrial relations (IR) in construction productivity has been neglected. This is despite countries with highly unionised workforces, such as Australia and Canada, often attributing the industry’s relatively low productivity to its confrontational IR environment. This paper explores how construction project managers and operatives in Australia interpret what has become a highly divisive IR debate and how this influences their IR behaviour. A survey of 92 construction project managers and operatives reveals multi-theoretical perceptions influenced by both pluralist and radical IR theories. While unions are seen as beneficial to project safety and work hours, they are perceived as detrimental to productivity, although there is uncertainty about how this relationship works. It is concluded that improvements in construction project productivity are unlikely to be achieved by IR legislation alone but through a more complex multidimensional bargaining lens where project managers and operatives develop mutually beneficial shared solutions.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.953
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.023
GPT teacher head0.246
Teacher spread0.223 · 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 designTheoretical or conceptual
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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Management Procurement and LawSame topicLabor Movements and UnionsFrench-language works237,207