MétaCan
Menu
Back to cohort
Record W2780910901

Comparative procurement methodology analysis in Australia

2010· article· en· W2780910901 on OpenAlexaboutno aff
Peter E.D. Love, Jim Smith, Michael Regan

Bibliographic record

VenueBond University Research Portal (Bond University) · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic Procurement and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessComputer science
DOInot available

Abstract

fetched live from OpenAlex

A comparative review of procurement methods was undertaken for the purpose of objectively determining the relative strengths and weaknesses of the principal methods for the state procurement of economic and social infrastructures. The study concerned procurement alternatives commonly used with large or complex projects and available to government, including: o In-house provision using a state agency or works department o Traditional procurement o Outsourcing o Build own operate and related forms of asset procurement o Alliance contracting o Public private partnerships. Around 90% of state procurement in the late 1980s was traditional which employs a comprehensive input specification, a lowest price tender selection process, separation of the design and construction components of the project and an adversarial approach to contractual relationships. The main measurement methods were delivery on time and within budget. As traditional procurement is mainly concerned only with the delivery of assets, most performance measures concern the timeliness and cost of delivery and these are mainly applied at commissioning. Tender evaluation criteria may take into account the qualitative aspects of bids such as the bidder's credit strength, expertise and track record. However, these values are generally subordinated to price and few traditionally procured projects are evaluated again during their service life. The development of a comparative procurement methodology involved a comparison of quantitative and qualitative outcomes. The evidence was sourced from the procurement outcomes of 124 economic and social infrastructure projects commissioned by governments or state agencies in Australia, Canada, New Zealand and the United Kingdom.

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)0.041
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.057
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0180.031
Science and technology studies0.0030.003
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.221
GPT teacher head0.376
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueBond University Research Portal (Bond University)Same topicPublic Procurement and PolicyFrench-language works237,207