MétaCan
Menu
Back to cohort
Record W2981894233

A Brief Appraisal and Analysis of Existing Practices in the Contracts of Infrastructure Projects of Indian Railways

2016· article· en· W2981894233 on OpenAlexaboutno aff
M. Chittaranjan, Kedarnath Prasad

Bibliographic record

VenueJournal of Construction Engineering Technology & Management · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsProcurementCall for bidsBiddingWork (physics)BusinessContext (archaeology)Project managementContract managementProcess managementOperations managementEngineeringMarketing
DOInot available

Abstract

fetched live from OpenAlex

Railways occupy a prominent position in the infrastructure of India. Existing practices in the project management and infrastructure development with special reference to the Indian railways have been brought out with some details in this paper. Tendering phase is very important in the project construction and maintenance of major railway related works. The principles and practices in the public procurement of projects are examined and suitable improvements in the bidding evaluations are suggested. The success and failure factors of any project are analyzed with a view to understand the dynamics of project management in the Indian context. The system improvements which are the need of an hour to make the Indian railways a more viable and vibrant organization are suggested in detail. A preliminary study of the bidding models in the construction industry as practiced in the US and Canada have been brought out for comparison. The importance of ethics, integrity and work culture in the project management as essential component is underlined apart from fool proof tendering practices. Keywords: Infrastructure, contracts and tenders, emerging practices, success attributes, system improvements

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)0.002
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.043
GPT teacher head0.344
Teacher spread0.301 · 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 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Construction Engineering Technology & ManagementSame topicConstruction Project Management and PerformanceFrench-language works237,207