Translating HCI Research to Broader Audiences: Motivation, Inspiration, and Critical Factors on Alternative Research Outcomes
Bibliographic record
Abstract
Alternative Research Outcomes (AROs) go beyond traditional academic publications, taking diverse forms such as documentaries, DIY tutorials, or exhibitions. With growing recognition of the need for more inclusive and contextually appropriate research dissemination, AROs are particularly relevant in HCI and design research. Yet, little has been discussed on why it is important to work on AROs. What are key qualities of AROs? How can the HCI community benefit from learning more about creating AROs? By analyzing six case studies, we propose four qualities of AROs and demonstrate how they emerge in the timeline of a research project. We argue AROs can be adapted to diverse audience needs and share research insights that may extend beyond the original research goals. Our work contributes to a deeper understanding of how AROs can support inclusive research dissemination practices, enabling HCI researchers to engage broader audiences and extend the relevance of their work.
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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 | 0.253 | 0.455 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.029 | 0.022 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".