Government Public Access Centres (PACs): A beacon of hope for marginalised communities
Bibliographic record
Abstract
Notwithstanding massive injections into broadband infrastructure, large sectors of the world’s population remain without access to the internet. One of the strategies employed by Government and the private sector to address the digital divide has been to provide basic computing services and internet access to communities in the form of public access centres (PACs) or otherwise known as telecentres. Over the years PAC related interventions have been subjected to much introspection, and critique, given a number of examples of failures. This paper examines a PAC program in South Africa which has been running with success for ten years. The paper reports on a study which includes data collected from more than two thousand four hundred users of PACs. The findings provide critical insights into the value proposition of PACs for communities in impoverished areas and whether this is still relevant from a policy perspective to tackle the digital divide. The findings also provide insights into the profile of users; the factors which impact on their choice of a PAC as an internet access point; and the extent to which there is a reliance on PACs. The most revealing finding from the study indicates that PACS provided the average user with something more than just an internet access point. The study has determined that PACs have a significant effect on the hopefulness a citizen has for his or her self, community and country.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".