Auto-Personalization: Theory, Practice and Cross-Platform Implementation
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
In an increasing digital society, access to information and communication technologies (ICT) is no longer just helpful but has become a necessity. However, the human interfaces appearing on these ICT (and increasingly, even common household products) are beyond of the abilities of many people with disability, digital literacy, or aging related limitations. Access to these ICT is essential to these individuals yet it is not possible to create an interface that is usable by all. This paper introduces a new approach to auto-personalization that is based on the development of the Global Public Inclusive Infrastructure (GPII). The GPII is a new international collaborative effort between users, developers and industry to build a sustainable infrastructure to make access to all digital technologies technically and economically possible, including access by users who are unable to use or understand today’s technologies. Based on a one-size-fits-one approach, the GPII uses auto-adapting mainstream interfaces, and ubiquitous access to assistive technologies when mainstream interfaces cannot adapt enough, to provide each user with the interface they need. The GPII has three main components: a mechanism to allow individuals to easily discover which interface variations they need and then store it in a secure way on a token or in the cloud; a mechanism to allow them to use these stored needs and preferences to automatically adapt the interfaces on the digital technologies they encounter, anywhere and anytime; and a resource for developers (mainstream and assistive technology) providing the information and tools required to develop, disseminate, and support new access solutions more simply, more quickly, and at lower cost.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 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