Legal analysis of the use of fake news in the political process between 2018 and 2021
Bibliographic record
Abstract
The objective of this master's thesis is to analyze the influence of fake news on the Brazilian political scene, more specifically in the context of the 2018 elections and the Jair Bolsonaro government between 2019 and 2021 in its management of popularity and its administration of the Covid-19 pandemic. The analysis will be focused on the response of the Brazilian legal system while using the Canadian legal system as a point of comparison. The text also focuses on how fake news works and how it was used in the context of the 2018 elections and later during Jair Messias Bolsonaro's government until August 2021, when this study was completed. In addition to factual analysis, this thesis will present the legal elements that aim to protect the privacy, security, transparency, use and responsibility of private data information, as well as the devices that can be used to combat the use of false news to manipulate the masses. This will be analyzed in the scenario of electoral campaigns, official government announcements and public policies in the Brazilian context and will be compared to the Canadian context. Furthermore, combined approaches from science and literature will be explored to reflect on the normative concerns posed by global challenges and local risks caused by mass manipulation, invasion of privacy, radicalization, use of personal data and data collection. Conclusively, we will verify whether the current instruments are efficient and sufficient to prevent, curb and punish the aforementioned practices. The results will reveal that even if the current instruments are efficient, they are not enough and need to be complemented with more flexible laws that are closer to the digital reality, with public policies and digital education for the population in general.
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 | 0.006 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".