Társadalmi innováció a városi desztinációk versenyképességének szolgálatában. Fókuszban az éjszakai gazdaság hatásainak menedzselése
Bibliographic record
Abstract
The development of the night-time economy has improved the destination competitiveness of many big cities. Income and tourism experiences, in particular, have been the most affected by this change. However, managing the potential negative impacts and harmonising the interests of stakeholders represent challenges. The purpose of this article is to show that a destination can only be competitive if it can serve the long-term well-being of both visitors and locals. In this research the authors provide an overview of the problems of the night-time economy and analyses the available solutions. Urban politics and local political forces are determinant factors that influence the management of a creative city. Furthermore, they focus on the solutions provided by social innovation. which involves the learning and cooperation of locals. In this paper, all these aspects are analysed in the context of Budapest's ruin pub quarter, which is nowadays the focus of debates as the negative effects of the night economy have not yet been effectively resolved. The impact of the night-time economy is examined through qualitative interviews in the quarter with locals and visitors to crystalize the most important issues and solutions as well as by mapping the characteristics of recent civil initiatives which are the driving force of social innovation due to the limited effectiveness of the measures of urban policies.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".