Smartphones as Alternatives to Computers for Learning and Collaboration in a Multinational Disability-Inclusive Community of Practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it