Brazilian Digital Inclusion Public Policy: achievements and challenges
Bibliographic record
Abstract
Brazilian Digital Inclusion Public Policy: achievements and cha This article presents the achievements and challenges of the digital inclusion public policy in Brazil from the perspective of agents working within the Federal Government. It starts with statistics on access to ICT infrastructure in general as well as the actual location of Internet use and socio-economic profile of the users. These indicators relate to the Federal Government strategies to face the digital divide. Three main strategies guide the implementation of the digital inclusion public policy: home access, school and community access centers. Effort is made to supply equipments, connectivity, human resources and capacity building programs, monitored by constant evaluation and indicator building processes. Among many ongoing initiatives, the Computers for Inclusion project (inspired by the Canadian Computers for Schools) and the National Digital Inclusion Observatory are explained in detail. The major challenges are then presented: broadband access in all regions; funds for the maintenance of existing infrastructure; local content production; qualified digital inclusion agents; participatory steering mechanisms for programs, community centers and schools; expanding the numbers of digital inclusion units; co-responsibility among Federal, State and Municipal levels. The conclusion ponders that while many results were achieved in implementing and measuring the strategies for bridging the digital divide, overcoming the stated challenges needs the effective involvement of Government in all its levels in partnership with society.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".