Lessons from the field: What researchers learned from evaluating ICT platforms for rural development and education
Bibliographic record
Abstract
The field of information and communication technology for development is a field constantly changing as new ICT tools emerge and new knowledge gained by field researchers while performing their duties. The research problem: The field ICT field is littered with examples of failed projects because field researchers did not know the best way to carry out their work. The paper is about knowledge imparted by six monitoring and evaluation field researchers after working for almost eight years, from 2010 to 2018, in ICT platforms projects. These platforms were deployed across South Africa’s remote rural areas. The work followed interpretivism as its philosophy and was underpinned by qualitative research methods. Written projects reports, face-to-face interviews and questionnaires were used to collect data and also to triangulate the findings. The participatory evaluation formed the basis for the complete understanding of the finding. (i) Planning; (ii) Deployment; and iii) Usage were found to be critical elements for a successful implementation of ICT platform. Although well planned, numerous lessons were still learned for the benefit of future projects.
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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 | 0.104 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".