Bibliographic record
Abstract
As technology use permeates many parts of society there are still groups where the penetration of technology is low: adults with little exposure to technology during their traditional learning years, users from lower SES, lower education levels, resulting in a digital divide between the digital haves and have-nots. This paper presents a community-based, mixed methods research project that endeavored to study the phenomenon of digital divide through a set of theoretical frameworks: Rawls’ principles of justice as fairness provided the overall social justice umbrella, Sen’s capability approach grounded the study in the specificities of learners’ lives and acknowledged learner diversity, and Horton’s cultural education, Freire’s critical consciousness, and Eubanks’ critical technology education provided the pedagogical lens to understand the importance of the critical learning process in digital education. The findings from the study support the concept of situated or contextual technology that seeks to increase the benefits of technology for adult learners while providing them the tools to manage complex digital environments through relatable instruction, user-centric design for technological tools and interfaces, and more robust government action in alleviating the digital divide through well-designed digital literacy programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".