Connecting in real space : how people share knowledge and technologies in cybercafés
Bibliographic record
Abstract
We examine how the internet brings people together not virtually over digital networks but physically while co-located in public spaces. In particular we are interested in how people in cybercafés share and collaborate with others who are physically present in the facility at the same time. We hypothesize that both explicit and implicit collaboration occurs among co-present internet users – at times intentional and purposeful while in other cases accidental, fleeting or voyeuristic. Public shared internet facilities are particularly important in low-come settings such as found in Africa. To examine this hypothesis in an African context we conducted a survey of 75 computer users at a major cybercafé, Busy Internet, in Accra, Ghana. We found that more than one-third of respondents reported some significant form of collaboration and computer sharing with friends, family members, business associates, and even strangers while in the café. Of those respondents reporting computer sharing one-half reported gaining knowledge and learning from the other user as their primary reason for sharing while only a small minority sited purely economic reasons for sharing. Those respondents who shared computers typically came to the cybercafé with more friends or associates, and generally had a better view towards collaborative group work and broader forms of interaction while in the café compared to the nonsharing respondents.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".