Designing for Adaptation to Novelty and Change: Functional Information, Emergent Feature Graphics, and Higher-Level Control
Bibliographic record
Abstract
Ecological interface design (EID) is a theoretical framework that aims to support worker adaptation to change and novelty in complex systems. Previous evaluations of EID have emphasized representativeness to enhance generalizability of results to operational settings. The research presented here is complementary, emphasizing experimental control to enhance theory building. Two experiments were conducted to test the impact of functional information and emergent feature graphics on adaptation to novelty and change in a thermal-hydraulic process control microworld. Presenting functional information in an interface using emergent features encouraged experienced participants to become perceptually coupled to the interface and thereby to exhibit higher-level control and more successful adaptation to unanticipated events. The absence of functional information or of emergent features generally led to lower-level control and less success at adaptation, the exception being a minority of participants who compensated by relying on analytical reasoning. These findings may have practical implications for shaping coordination in complex systems and fundamental implications for the development of a general unified theory of coordination for the technical, human, and social sciences. Actual or potential applications of this research include the design of human-computer interfaces that improve safety in complex sociotechnical systems.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".