Engajamento corporativo nos programas de privacidade das empresas da Nova Economia
Bibliographic record
Abstract
O artigo examina o cenário da nova economia e os modelos de gestão que se disseminaram para obtenção do posicionamento no mercado ágil e hostil das Startups, traçando um paralelo com a necessidade de implantação de ferramentas e estruturas de governança relacionadas à proteção e privacidade de dados pessoais, conforme a Lei Geral de Proteção de Dados Pessoais - LGPD (Lei nº 13.709/2018). Assim, discute-se a organização dessas empresas, suas características, o apetite a riscos e a cultura dos seus colaboradores, identificando os pontos relacionados ao aculturamento interno no processo de amadurecimento e responsabilização frente a um novo processo relacionado aos critérios de governança. A identificação destes tópicos, relaciona-se com a relevância e necessidade da adequação das empresas da nova economia à LGPD, as eventuais sanções e oportunidades observadas, as dificuldades na implantação e algumas estratégias utilizadas para melhorar a adoção dos princípios de privacidade e proteção de dados.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".