Ghana’s blockchain scene on WhatsApp : A space for convergence and divergence
Bibliographic record
Abstract
Social media sites with global reach like Twitter and WhatsApp are playing a paramount role in the communication, socializing, and business practices of Ghana’s blockchain community. Specifically, WhatsApp has become the platform powering the principal instantiation of blockchain in Ghana, which is trading and investing in cryptocurrencies. Considering the country’s high internet fees and sometimes unreliable network access, WhatsApp is a particularly endearing platform to facilitate the blockchain scene due to the low internet data usage that it requires. Drawing on empirical research data from 33 semi-structured interviews with blockchain enthusiasts in Ghana, this paper analyzes the particularities of blockchain’s adoption and spread in its primarily virtual scene. Key to this examination is the consideration of the affordances and constraints of WhatsApp as the primary spatial frame driving and shaping blockchain’s adoption and use in Ghana. As coagents with the digital sphere they transact and interact on, members of the blockchain community are collectively and individually perpetuating processes of knowledge creation and communal exchanges interspersed with values of competitive innovating.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".