The Reality of Reality-Based Interaction
Bibliographic record
Abstract
Frameworks such as Direct Manipulation or Instrumental Interaction have been an important force in HCI research. Evaluating the impact of frameworks can identify whether and how a framework was used, how it has evolved, and what trends have developed over time. However, studying the impact of such theoretical contributions requires consideration of various perspectives and level of impact. As a case study for investigating the impact of theoretical work in HCI, we present our evaluation of the impact of the Reality Based Interaction (RBI) framework, introduced by the authors in 2008. We provide our findings about the impact of the framework both on contemporary research, through content-based citation analysis, and in HCI education, through a survey we conducted on emerging interaction frameworks. The article contributes a comprehensive methodology for evaluating the impact of frameworks through our twofold approach: content-based citation analysis, including the design of a new citation typology; and a survey on the use of frameworks in education using a taxonomy of learning goals. We also consider the role of frameworks in HCI as well as the future of the RBI framework.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".