Towards a Scalable Digital Skills Training Architecture for Resource-Constrained Environments: The Case of Ayitic Goes Global in Haiti
Bibliographic record
Abstract
In developing countries such as Haiti, which are marked by high unemployment and gender inequality, online education has the potential to change lives. Returns on education are particularly high in Information Communications Technology (ICT)-intensive jobs and IT outsourcing offers opportunities for remote employment, providing alternatives for economic diversification and job creation that are particularly relevant for youth and women. However, the problem faced by many developing countries, is that traditional models, frameworks, architectures, and platforms for online learning do not lend themselves well to their context and, therefore, it is important to develop context-specific platforms. This need for suitable platforms has motivated the research question that this paper seeks to address, that is: What is the appropriate architecture that supports learning strategies for delivering scalable digital skills training in a resource-constrained environment? We propose an architecture that was developed specifically for blended learning in resource-constrained environments and describe how a prototype for this was designed, built, and deployed in Haiti. The initial responses to the application of the architecture, including the testimonials of the participants and the interest expressed by other countries of the region in adopting the proposed architecture, have been extremely positive.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".