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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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