Applying a Community Informatics Approach as Part of Rehabilitation in US Prisons
Bibliographic record
Abstract
The United States Government has acknowledged that digital literacy is a vital component of 21st century education and civic engagement. As such efforts are being made to draw in segments of the population that are negatively affected by the digital divide. Not included in these efforts is a community of individuals, most of who have the lowest literacy rates and come from the lowest income strata in society—prison inmates. Despite a few scattered attempts, these individuals have virtually no access to resources and training that would create the condition by which when released they will be able to complete commonplace tasks that depend on an assortment of digital technologies. Discussions around access are often confronted with scepticism by prison administration and citizens alike. This paper uses the information obtained about National and Washington state specific prisons to describe the landscape and the importance of preparing incarcerated individuals to confront an information society. Finally, using the Access Rainbow, the paper brings forth obstacles related to introducing a level of access and training that will prepare inmates to be productive participants in a technological based socioeconomic system after release from prison.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".