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Record W1826990030 · doi:10.3968/7453

On the Construction of Chinese Government Procurement of Public Service Assessment System

2015· article· en· W1826990030 on OpenAlexvenueno aff
Hongguang Wang, Weiming Fang, Shi Chunyuan

Bibliographic record

VenueCross-cultural communication · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic Procurement and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsProcurementGovernment (linguistics)PoliticsGovernment procurementPublic servicePublic administrationBusinessPublic valueService (business)Value (mathematics)Public relationsPolitical scienceMarketingLawComputer science

Abstract

fetched live from OpenAlex

To improve the way of governments to provide public services is a hot spot in academic and political studies in recent years. CCP Eighteenth Congress Report points out that, “we must change the means of providing public services through reasonable approaches, while using reasonable methods to improve the existing social management system.” Chinese society has entered a stage of rapid economic and cultural development, and only based on the fulfillment of the principles and policies implemented by the government since the eighteenth Congress can we truly promote the socio-economic and cultural development. By analyzing the problems in current government procurement of public services, this paper puts forward corresponding solutions so that these policies can help the government to create more public service value.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.004
Science and technology studies0.0030.002
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.053
GPT teacher head0.321
Teacher spread0.268 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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