Bibliographic record
Abstract
This paper focuses on evaluating an information and communication technology (ICT) intervention promoted as a pro-poor telecentre initiative in rural Ghana. Our evaluative tool is the Design Reality Gap (DRG) framework used to analyse the Community Information Centre (CIC) initiative in Ghana. Data were collected through a qualitative multi-site case study. By tracing the linkages between the investment and outcomes, we found a worrying trend of failed implementations and sustainability, although implementers did sustain efforts at planning new initiatives. Based on the findings, we argue that the CIC initiative in Ghana is a failing ICT intervention. We also found that the tailored DRG approach allowed us to tease out the nuances that account for the CICs' status. We conclude by proposing gap closure measures for the failing intervention. This paper contributes to ICT evaluations by demonstrating the utility of the DRG framework in evaluating one of the most significant pro-poor ICT initiatives in lower-to-middle-income communities: telecentres. This research also contributes to the current ICT literature by enhancing our current knowledge about publicly accessible ICT facilities in an under-investigated setting, and further offers an approach to telecentre evaluations in similar contexts inspired by the DRG model.
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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 | 0.105 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".