ICT, Local Government Capacity Building, and Civic Engagement: An Evaluation of the Sample Initiative in Ghana
Bibliographic record
Abstract
Abstract This paper evaluates a local Regional Network (LRNet) in one of Ghana's administrative regions; the purpose of the network is to enhance the capacity of the local government to perform its functions, promote transparency, and serve as a mechanism for civic engagement in the political process. I adopt Zhu's WSR approach as a conceptual model for this analysis, which examines, within a concrete setting, the nature, challenges, and outcomes that emanate from the intersection of the dual paradigm shifts in information technology and the reinvention of government. The case study concludes that there is a significant expectation-perception gap between the project's intent and its outcomes. The findings strongly support the view that computers by themselves cannot achieve organizational goals if the necessary enabling environment does not support them. It is clear from this study that ICTs do not function in a socio-cultural, political, and economic vacuum. Their efficacy is contingent on the various forces and realities that coalesce to shape the environment into which they are introduced. While the technologies may be designed in a way that allows them to perform certain functions, it is the decisions, orientations, and attitudes of human beings, as well as the resource capabilities of the organizations, which ultimately determine the success of IT undertakings. Therefore, organizations employing the ICTs must appreciate the limitations of an instrumental perspective that focuses only on the "digital messiah" as the panacea for organizational problems and the sole catalyst for government reinvention.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".