Locked Up and Blocked Out: The Digital Divide for Formerly Incarcerated Women in Canada
Bibliographic record
Abstract
In Canadian prisons and jails, populations are not able to access the internet, and many other essential technologies. Several research studies have examined the impact of the digital divide on incarcerated populations in the United States and other countries around the world (Barreiro-Gen & Novo-Corti, 2015; Reisdorf & Rikard, 2018). This study will expand on the current research by examining the impact of restrictions to internet access in Canadian prisons on the lives of formerly incarcerated women in Canada and, more specifically, how these restrictions affect their ability to reintegrate into society after the period of incarceration. The methodology of this research will be qualitative, and data will be collected through semi-structured interviews with individuals who have a variety of different experiences with the women’s correctional system in Canada. This study will address major areas of research in the field of study that addresses the digital divide, including the learning and development of digital skills, and how different identities can intersect to impact the way individuals experience the digital divide. Through constant comparative content analysis, this study describes the experience of the digital divide, how it both persists and develops from the time of incarceration to life post-incarceration, and how it can compound other types of barriers faced by women who have been incarcerated in our 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.043 | 0.013 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".