Designing for development: Understanding One Laptop Per Child in its historical context
Bibliographic record
Abstract
We argue that the One Laptop Per Child (OLPC) can be better understood by examining the general history of development communication and, specifically, through a historical debate between communication scholars Ithiel de Sola Pool and Herbert Schiller. Although originally conducted around broadcast media, the Pool-Schiller conversation identifies questions still relevant to contemporary information and communication for development (ICT4D) projects like the OLPC. Our analysis of their debate identifies five key questions we can apply to the OLPC or any given ICTD4D project: where does change happen? How does change happen? What obligations do designers and researchers have as change agents? What is the role of technology in change? What is the relationship between change, technology and international development? Equipped with this framework, we argue that one place to see OLPC's answers to these Pool-Schiller questions - and, thus, an understanding of OLPC development ideologies - can be found in a textual analysis of the OLPC software design guidelines. This preliminary analysis suggests that OLPC sees the child as the agent of change and the network as the mechanism of change.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.040 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".