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Record W2298772594

Life Cycle Costing in Defence Acquisition: The Challenges of Transforming Complex Aspirations into Factual Ground Realities

2016· article· en· W2298772594 on OpenAlexaboutno aff
Sandeep Verma

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic Procurement and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsProcurementContext (archaeology)Activity-based costingGovernment (linguistics)Process (computing)WorkforceBusinessPublic administrationOperations managementEconomicsAccountingPolitical scienceEconomic growthMarketingComputer science
DOInot available

Abstract

fetched live from OpenAlex

Life cycle costing (LCC) is an extremely alluring procurement technique for government contracting professionals in developing countries, given its potential for reducing budgetary outgoes through lowered total cost of ownership during the entire life cycle of procured public assets. However, proper implementation of LCC in a public procurement context inherently requires strict cost visibility, verifiability and contracting discipline during comparative evaluation of proposals as well as during contract administration and implementation, making it an extremely difficult and challenging process, particularly in developing countries with relatively unskilled acquisition workforce and unresponsive legal systems as compared to developed country jurisdictions. Within this background, this short academic note explores certain LCC techniques employed under India’s defence procurement procedures, while also attempting quick comparisons with NATO, US and Canadian guidance on the subject. underlying intent is to use rigorous academic analysis for the purpose of formulating recommendations for suitable reforms in India that could perhaps also be useful for other developing countries interested in implementing LCC-based procurement for obtaining effectiveness and efficiency in their defence acquisition programmes. An initial draft of the paper was published in the journal The Administrator [January 2015, Vol. 56 No. 1, pp. 1-13].

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.453

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 communication0.0000.002
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 head0.088
GPT teacher head0.275
Teacher spread0.187 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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