Bibliographic record
Abstract
As we are witnessing an increase in multifunctionality of interactive devices, two problems are taking shape in user interface (UI) design: first, the problem of complexity, and second, the problem of fragmentation (Kljajevic, in press). The former is reflected in the fact that multipurpose interactive devices usually have interfaces that do not allow easy access to new functions and features, rendering the increased functionality useless. The second problem is related to the fragmentation in the current research paradigms and testing trends that inform UI design. These paradigms and trends stem mostly from psychological theories that focus on only some specific aspects of user-interface interaction. While it is important to investigate such topics in detail, it is even more important to look at the totality of the interaction and determine the principles that operate in it. An integrative approach to UI design has the potential to solve both problems. Such an approach has two components: a top-down and a bottomup component. Its top-down component deals with a small set of basic cognitive principles that operate in interactive reality and therefore need to be recognized at the level of UI design. The principles are built into a cognitive architecture—a wide theoretical framework that corresponds to the human cognitive system—whose constraints prevent proliferation of implausible theories, which solves the fragmentation problem.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".