Bibliographic record
Abstract
The evolution of internet access and use in Brazil in the direction of social inclusiveness and to guarantee uses that promote individual and community development is the focus of the present paper. Previous evidence on the subject initially presented indicates the prevalence of contrasting aspects: some outstanding positive initiatives and results towards democratization of the internet, as well as the maintenance of great digital inequalities. New evidence on the evolution of internet access and use is also discussed herein, based on analyses of longitudinal data obtained from the Brazilian Census Bureau’s Annual Survey (IBGE/PNAD, 2005, 2008). After describing aspects of increase in access to the internet, we focus on the evolution of different types of everyday life internet uses, particularly those that might contribute to individual and community development. How accesses and uses are gradually incorporating the less privileged sectors of the Brazilian adult population is also considered in the analyses. The results obtained reinforce the previous contrasting evidences: outstanding growth in access and in diversified uses are observed - suggesting intensive appropriation of internet technology and resources by the population - as well as the persistence of great inequalities. These circumstances indicate that the digital divide in Brazil is still a great challenge to be faced through comprehensive and long-term policies and initiatives.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".