Digital Inequalities to Digital Inclusion in Online Learning: Viewpoints of <scp>LIS</scp> Educators Seeking to Bridge the Disparities
Bibliographic record
Abstract
ABSTRACT Academics argue that the COVID‐19 pandemic has limited students' ability to learn, with significant digital inequities occurring between students from the global North and the global South. Students and academics from developing nations encountered particular challenges and difficulties with the move toward online styles of learning. Much like their colleagues from developed countries, they were unprepared for this predicament, but on top of the crisis context, deeper issues were having to do with digital inequalities and disparities that were exacerbated by the inadequate digital infrastructure (smart devices/gadgets, internet access, and speed) and online interaction abilities and practices. The goal of this panel is to address the pressing issue of digital inclusion in online education, specifically the broader challenge of ensuring that online education is accessible to all. As information researchers continue to work towards enhancing online learning, it is crucial to address the disparities in the sharing of information and knowledge and to bridge the gaps that exist across communities and nations. The panelists (three of whom work in developed countries and two in developing countries) will relate their experiences and viewpoints thus bringing their knowledge to bear in examining the concepts of digital inequality and digital inclusion. The rest of the session will be devoted to discussions and brainstorming with attendees around these issues, with special attention being given to perspectives that seek to bridge the disparities and promote inclusion in education.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.022 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.009 | 0.015 |
| 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".