Local Learnings: An Essay on Designing to Facilitate Effective Use of ICT s
Bibliographic record
Abstract
In this essay, we explore some of the details of what it takes to own, use and derive benefit from information and communication technologies, with a focus on regions where ICT adoption and use is especially low. We begin with a fairly meticulous description from our ethnographic work to which we'll refer throughout the paper. Though we consider this particular instance, we note that it represents of a wide range of instances from our ethnographic work in homes and businesses over several years in Brazil, Costa Rica, Chile, Ecuador, Bolivia, Peru, Korea and India. Our goal in this paper, however, is to change the conversation from discussions of infrastructure and capacity building to considerations of local, lived conditions in actual homes and actual businesses to suggest design alternatives that make effective use of ICTs more amenable to various locales. We offer two design directions especially for high tech corporations: Designing for Locus of Control and Designing for Local Participation. Along the way, we'll argue to re-frame of the current conception of "digital divide", putting the burden not on those with limited access, but on limited understanding within the high tech industry.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".