Auto-Personalization: Theory, Practice and Cross-Platform Implementation
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.
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 | 0.031 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".