Smartphones as Alternatives to Computers for Learning and Collaboration in a Multinational Disability-Inclusive Community of Practice
Bibliographic record
Abstract
For decades, computers have facilitated many complex tasks, but not everyone has access, especially in low-income countries. However, the current affordability and increasing use of smartphones make them a good alternative for education and research collaboration. This Partnerships for Inclusive Research and Learning (PIRL) study explores smartphones' role in learning and research collaboration in a multinational community of practice (CoP) involving participants in the Global South and the Global North. The PIRL study used a survey and interviews to realize the optimal use of information and communication technologies (ICTs) in a CoP for knowledge provision, research, and professional development in a disability-inclusive development (DID) context. The CoP included some academic and community researchers with disabilities. Survey results showed that 50% of the PIRL CoP participants from African countries cannot use computers as much as wanted because they are unaffordable or lack reliable or affordable internet. All respondents from the Global North could use the internet and computers as much as they wanted, results that reflect the digital divide. Since PIRL CoP participants in the Global South are disadvantaged in computer use, they turn to more affordable smartphones for collaboration and learning. However, small devices challenge the performance of complex tasks like collaborative writing, coding interview transcripts, and contributing effectively to teamwork. This study provides some recommendations to improve collaboration in such situations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".