Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract—Non-governmental organizations (NGOs) working in disadvantaged communities have a variety of data-collection and analysis needs, for example, for performing surveys or monitoring programs. Because much of this data collection occurs in environments with insufficient IT support and infrastructure, and among populations not always comfortable with technology, paper forms rather than electronic methods remain the predominant means for data collection. We consider the design of machine-readable paper forms for NGOs. We first examine the unique needs of NGOs that interact with underprivileged populations through interviews with eleven organizations and an in-depth investigation of one NGO’s specific form-filling requirements. These explorations led to a focus on numeric forms – forms with questions requiring responses largely constrained to numbers. We then present an experiment which evaluates how a variety of formats for numeric data would fare with users from backgrounds similar to those who might fill out such forms. Our goal was to balance the tradeoff between ease-of-use among our intended population and machine readability. Combining the results of the experiment with an analysis of machine-readability from a technical perspective, we propose the best numeric input methods for different NGO form filling requirements. Index Terms — machine-readable forms, paper forms, input methods, ICT for development I.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it