Bibliographic record
Abstract
Abstract Hackathons are short-term events at which participants work in small groups to ideate, develop and present a solution to a problem. Despite their popularity, and significant relevance to design research, they have only recently come into research focus. This study presents a review of the existing literature on the characteristics of designing at hackathons. Hackathon participants are found to follow typical divergence–convergence patterns in their design process throughout the hackathon. Unique features include the initial effort to form teams and the significant emphasis on preparing and delivering a solution demo at the final pitch. Therefore, hackathons present themselves as a unique setting in which design is conducted and learned, and by extension, can be studied. Overall, the review provides a foundation to inform future research on design at hackathons. Methodological limitations of current studies on hackathons are discussed and the feasibility of more systematic studies of design in these types of settings is assessed. Further, we explore how the unique nature of the hackathon format and the diverse profiles of hackathon participants with regards to subject matter knowledge, design expertise and prior hackathon experience may affect design cognition and behaviour at each stage of the design process in distinctive ways.
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 | 0.120 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.024 | 0.046 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".