Modeling the Impact of the Creative Industry on National Sustainable Educational Development in France
Bibliographic record
Abstract
The principal aim of this article is to delineate the models that illustrate how creativity impacts a country's sustainable development.The object of this study is the creative industry within various domains of public life in France, including the sports sector.It also encompasses the sustainable development system and its essential indicators, such as the evolution of creative elements across different industries, and the workforce specializing in these sectors.The scientific task at hand is the formulation of a model demonstrating the sustainable influence of creative industries on the sustainable development system.The methodology adopted for this research integrates "cost-output" modeling methods and a computational general equilibrium model.As an outcome of this investigation, we present the cost-output model and its multipliers, as well as a computational general equilibrium model.The innovative aspect of this article lies in the calculated results showcasing the sustainable effect on the country from the creative industry.The study's boundaries are defined by the sustainable development system of France alone, as well as the chosen research methods and the types of creative industries considered for investigation.Future research prospects should focus on probing the influence of creative industries from other sectors within the country, including an in-depth examination of the sports sector and other industries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".