A Set of Heuristic Measurements for Evaluating the Inclusiveness of a Technology
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
At a high level of abstraction,’ social inclusion’ can be defined as the extent to which an individual or community can fully participate in society and control their own collective destiny. There are large disparities in this, particularly in underdeveloped rural areas of the world. Information and communication technologies designed to address this disparity must take into account the many barriers in the use of technology that these communities face. We define an ’inclusive technology’ as a technology which overcomes the barriers to using technology inherent within a given community and increases the opportunities available to that community. We propose a conceptual model and a set of heuristic measurements for examining the ’inclusiveness’ of a technology with respect to a given community, and illustrate their use by applying them to two real-world projects. By proposing this model and set of measurements, we hope to achieve a better understanding of’development projects’ and create a systematic process and a framework to assist software engineers in designing and evaluating software based services intended to reduce the Digital Divide.
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 | 0.002 | 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 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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