EXPERIENCES OF DESIGN AT HACKATHONS: INITIAL FINDINGS FROM AN INTERVIEW STUDY
Bibliographic record
Abstract
Abstract Hackathons are short design events at which participants collaboratively progress through the entire design process. They pose opportunities for design research, but the existing research is limited, as is the understanding of design activity at hackathons. In our study, we summarize the hackathon design process of 10 interview participants from varying disciplines, levels of experience, and hackathon events. The summarized account reveals a decreased emphasis on the beginning phases of the design process, mainly problem definition, but an increased emphasis on the end, specifically the pitch portion of the event. These differences are mainly due to the limited time frame. We further assess the effect of time limitations at hackathons by comparing hackathons to other instances of design, emphasizing the impact of time constraints on iteration. We conclude our discussion with an exploration of the role expertise has on the design process by comparing the accounts of designers and developers.
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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 | 0.030 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".