Weaving the “mobile web” in the context of ICT4D: A preliminary exploration of the state of the art
Bibliographic record
Abstract
Abstract This paper considers the recent development‐related initiatives championed by the World Wide Web Consortium (W3C). More specifically, it examines the mandate of the Mobile Web Initiative (MWI) and Mobile Web for Social Development (MW4D) interest group with respect to their connections with the broader Information and Communication Technology for Development (ICT4D) movement, and, to a lesser extent, the concurrent focus on Free/Libre/Open Source Software (FLOSS). Through a close review and discussion of the technical literature, and an evocation of critical perspectives related to ICT4D, foundational material is introduced towards considering the emancipatory potential of these initiatives as a core research question. Ultimately, it is argued (albeit tentatively given the evolving nature of these very‐current projects) that this work remains important to the research community, but its integration within the broader agenda for international development requires further consideration. Accordingly, the paper concludes with a series of questions that can further inform research in this field.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".