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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.016
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 head0.279
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