EMPREENDEDORES DOS ESPAÇOS PÚBLICOS: ESTRATÉGIAS DE USO, MERCADORIZAÇÃO E CONSUMO EM FEIRAS GASTRONÔMICAS E CULTURAIS NA CIDADE DE SALVADOR
Bibliographic record
Abstract
This article analyzes the appropriation of public spaces in Salvador through gastronomic and cultural fairs, such as “A Feira da Cidade” and “Salvador Boa Praça.” Considering that these events attract a considerable audience and occupy squares, parks and open spaces in the city, engendering a process of commodification that utilizes the tactics (De Certeau, 1988) and meanings of the “creative economy” and entrepreneurship, this study seeks to understand whether these actions are establishing a neoliberal rationality over public spaces, reconfiguring social relations and intersubjectivities (Dardot; Laval, 2016). To this end, bibliographic research was conducted – including the construction of an analytical model – direct and participant observations, data generation in digital media using WebScraping techniques (Fernandes, 2017), and qualitative analysis with the Atlas.ti software. The results indicate that this rationality permeates not only markets and companies but also individual practices and urban uses. There is a predominance of these events in middle/upper-class neighborhoods, with curations that align products and attractions to the “desired” consumers, also functioning as incubators for new enterprises. Thus, the fairs transform spaces into loci of entrepreneurship and commodification, reconstructing social interactions and converting citizens into consumers and entrepreneurs. Companies, in turn, see economic potential in these events, adhering to the proposed logic. However, the practices undertaken tend to exclude the heterogeneity of the public and are marked by the commodification of the city, reflecting a neoliberal logic that influences the social and economic organization of the urban environment.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".