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Record W1870851074 · doi:10.3968/4321

Research on Promotion Strategies of Regional Soft Power: Take City’s Cultural Soft Power for Example

2014· article· en· W1870851074 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 · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobalization, Economics, and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsSoft powerConnotationPromotion (chess)Power (physics)Hard powerCultural industryCultural heritageService (business)Perspective (graphical)SociologyBusinessPolitical sciencePublic relationsEconomyMarketingChinaEconomicsComputer sciencePoliticsLaw

Abstract

fetched live from OpenAlex

This paper, based on clarification of connotation of soft power, analyzes elements of regional soft power,such as regional culture, public service, the quality of man power, regional image, regional communication and regional innovation, and then take city’s cultural soft power for example ,unfold the five key elements of the city’s cultural soft power, include the city’s spirit, the city’s historical and cultural heritage, the cultural industry, the fine literature and art and the cultural brands, public cultural service system. At last we put forward suggestions of promoting regional soft power from the perspective of the six elements thereof.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.190
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.018
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 head0.316
GPT teacher head0.418
Teacher spread0.102 · 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