Blogging for ICT4D: reflecting and engaging with peers to build development discourse
Bibliographic record
Abstract
Abstract Information and Communication Technology‐enabled Development (ICT4D) discourse relies upon the idea that ICTs can foster development in particular by encouraging wider participation in development initiatives. In this paper, we question how the blogging practices of development professionals shape such ICT4D discourse. Through a combination of interviews and analyses of blog contents, we examine two major purposes of blogging: reflecting upon development practices and engaging with a self‐selected audience. Our analyses reveal that these two purposes were interwoven in ways that contributed to making bloggers' ICT4D discourse innovative but oriented towards a small community of peers rather than a larger audience. Through blogging, development professionals refined their expertise on ICT4D. As they did so, they also generated a personal speaker's corner that primarily attracted like‐minded peers rather than promoting larger participation in ICT4D discourse. This research contributes to the emerging literature on social media practices by showing how blogging practices enable the formation of what a discourse is about, and by highlighting differences between perceived and actual levels of interactions between bloggers and their audience. The paper also adds to the ICT and development literatures by revealing that blogging practices can deepen ICT4D discourse, but that they do not necessarily enhance participation in development. Such insight is crucial for development professionals to develop realistic expectations of blogging for ICT4D.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".