Electronic Communication in Developing Countries: Explanatory Theory, Volume 2
Bibliographic record
Abstract
A few years ago, respondents in developing countries had never used the internet or even had electricity to charge a cell phone; but now, respondents overwhelmingly report that they are using computers and cell phones to send e-mail, play games, access information, listen to music, bank, develop literacy skills, and enroll in e-courses. Developing countries are using cellular telephones and internet interconnectivity even more than countries nearly saturated with these devices and conveniences. What can we learn from African, Asian, South American, Middle Eastern countries and even island countries like Jamaica, Maldives and the Philippines? This collection of data comes at a critical time for exploring shifts in communication practices that are occurring in all nations. It introduces explanatory theory from a student's viewpoint to complete the BRICS country overview and add 18 countries worthy of observation. Some are carefully watched to see if they pass over into developed country status. All are experiencing infrastructure problems. Their technology in many cases is leapfrogging into usage patterns seen in the US, Canada, and Western Europe. The purpose of this scholarship is to acknowledge the uniqueness of culture in each of the countries observed without attempting to impose a western framework of interpretation upon the communication behaviors. This is exploratory research accomplished by many who spoke the language of the country they investigated. We hope that this book inspires continued dialogue on the influences of electronic communication and falls outside the purview of readers' daily lives, providing a window into these developing nations.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".