A Champion in Our Midst: Lessons Learned from the Impacts of NGOs’ Use of the Internet
Bibliographic record
Abstract
Abstract Research investigating the use of Email and the World Wide Web in the South has focused solely on the users of the technology, ignoring the ability of those without connectivity to benefit from the outputs of their stakeholders’ Internet use. This paper examines the findings of an evaluation into the efficiency and effectiveness with which Internet‐equipped non‐governmental organizations (NGOs) were able to use the technology as a tool to assist their unconnected stakeholders, or those stakeholders which do not have connectivity but who are in a position to receive information which their supporting NGOs have acquired through the use of the Internet. Focusing primarily section dealing with the impacts of NGOs’ Internet use on their community stakeholders, the paper presents evidence to indicate that NGOs are sharing information acquired on the Internet with their unconnected community stakeholders, and cites specific examples from two of the three NGOs examined in this study. The paper also finds that the difference between those organizations which demonstrated a tendency to share Internet‐acquired information with their stakeholders and those which did not is two‐fold: those organizations which shared the information with their unconnected community stakeholders not only have both email and WWW access, but they also have experienced leaders in information technology (IT) to assist them in the integration of the technology into their programs. The findings of these three case studies illuminates the fact that without the presence of an Internet Champion, or staff who appreciate the value that ICTs can offer to organizations and their unconnected community stakeholders, such technologies – and the potential they hold for rural development throughout the world – will never be able to live up to the expectations which society has created for them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".