Sprawling of creative economy in Belgrade: How policies influence artistic and knowledge-based creative economies
Bibliographic record
Abstract
The 1990s were a decade of isolation, civil wars and deep economic crisis in Serbia. As a consequence, post-socialist transition started in 2000, after the ‘democratic revolution’. In any case, urban transformation took its place before political and economic changes. Following the other trends coming from the West, creative economy also became an important factor in the Serbian economy, as well as in its urban development. This article will try to reveal connections of the creative economy’s development in the Serbian capital Belgrade on one hand, and policies initiated and/or followed that development on the other. As creative economy includes a wide pallet of creative activities, attention will be paid on two specific activities. It is clear that on the beginning of a creative chain is art, and at the end knowledge- based creative activities. Two city quarters representing the embodiment of a spreading of the creative economy in Belgrade will be detected - a quarter with a strong focus on artistic activities, and another one which attracted many IT companies. We will see how policies influenced the sprawling of these two creative activities, what the inputs of newly developed governance models are, and what are the inheritances of socialist legacy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".