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Construction and Measure of the Evaluation Index System of Regional Soft Power - Taking Shandong Province as an Example

2012· article· en· W1749132707 on OpenAlexvenueno aff
Tingshan Song, Chen Shanshan

Bibliographic record

VenueStudies in sociology of science · 2012
Typearticle
Languageen
FieldMathematics
TopicAdvanced Research in Science and Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsYearbookSoft powerConnotationStatus quoIndex (typography)Measure (data warehouse)Regional sciencePower (physics)ChinaBusinessGeographyComputer sciencePolitical scienceData miningLibrary science

Abstract

fetched live from OpenAlex

Based on the concept and connotation of regional soft power, combined with the actual situation of the economic and social development in Shandong Province, this paper builds a strong operational regional soft power rating index system. Using the data of the Statistical Yearbook of the 17 cities in Shandong Province in 2008, z-score standardized methods and factor analysis and so on, finally the paper have a comprehensive evaluation and analysis about the status quo of regional soft power of the 17 cities in Shandong Province. Key words: Regional soft power; Evaluation index system; Factor analysis

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.279
GPT teacher head0.449
Teacher spread0.170 · 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 designSimulation or modeling
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

Citations1
Published2012
Admission routes1
Has abstractyes

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