Mapping in Intellectual Capital Measurement in Creative Industries in East Java
Bibliographic record
Abstract
In the context of global competition, competition not only occurs in the world of industry and trade, but also applies to creative businesses or more specifically creative industries which are industries that come from the utilization of individual skills, creativity and talent in creating welfare and employment use. Problems in managing SMEs and creative industries that have not been resolved are intellectual capital issues.One approach used in the assessment and measurement of knowledge assets (intellectual property / assets) is Intellectual Capital which consists of 3 main elements, namely Human Capital, Structural Capital, and Relational Capital. Creative industries are industries that are unique and emphasize creativity, innovation and utilization of individual talents need to get maximum management. The purpose of this study is to find a model, the appropriate components of intellectual capital, and to get a real picture of the rules of Human Capital; Structural Capital and Relational Capital for creative industries in East Java. The sample in this study is the owner / manager / leader of 5 creative industry sub-sectors in 9 cities in East Java (Surabaya, Pasuruan, Probolinggo, Mojokerto, Batu, Malang, Kediri, Blitar, and Madiun) with a sample of 45 as respondents. The analysis technique used in this study is the Spider Plot Diagram.Based on the results of the survey and studies, the aspect of mapping of intellectual capital in 5 (five) creative industry sub-sectors in East Java shows that relational capital is more dominant followed by human capital, and the lowest is structural capital.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".