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Record W4231880762 · doi:10.17722/ijme.v8i1.873

Performance of Gold Monetization scheme in India

2016· article· en· W4231880762 on OpenAlexvenueno aff
D. S. Selvakumar

Bibliographic record

VenueInternational Journal of Management Excellence · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsBullionMonetizationCommerceBusinessChinaGold standard (test)EconomicsGeographyMathematicsPolitical scienceLaw

Abstract

fetched live from OpenAlex


 India has topped with largest consumers of gold, next to china in the world. Indians prefer buying gold in the form of jewellery and coins rather than bullion. So, central government has come up with three gold schemes namely GOLD MONETISATION SCHEME, SOVEREIGN GOLD BOND and INDIAN GOLD COIN. The main motive of these schemes is to reduce the requirements of gold through imports. About 20000 tonnes of gold are idle with Indian households, temples, etc which is not being traded or monetized in the form of jewellery. This study attempts to scrutinize the three gold schemes in detail with pros & cons and people awareness towards such schemes particularly in Vellore District , Tamilnadu
 
 

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.036
GPT teacher head0.397
Teacher spread0.361 · 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

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