MétaCan
Menu
Back to cohort

The Research on the Soft Power of City Culture

2012· article· en· W2171878674 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
KeywordsSoft powerCohesion (chemistry)AppealPower (physics)Hard powerSociologyPolitical scienceLawChina

Abstract

fetched live from OpenAlex

The soft power of city culture is the core of the city soft power. It refers to the influence, cohesion and appeal that a city possesses, which is the main element of the competitiveness and interacts with hard power, for example economy and technology. The soft power of city culture contains the cohesion of city culture, the appeal of city culture, the innovation of city culture, the integration force of city culture and the radiation of city culture. To improve the soft power of city culture, we must treated the following relationships well: the relation between soft power and hard power, the relation between the soft power of national culture and city culture, and the relation between the soft power of culture in this city and in other cities. Key words : Soft power of city culture; Cohesion of city culture; Appeal of city culture; Innovation of city culture; Integration force of city culture; Radiation of city culture

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)0.013
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.064
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.472
GPT teacher head0.568
Teacher spread0.096 · 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