Presenting Hackathon Data for Design Research: A Transcript Dataset
Bibliographic record
Abstract
Abstract Hackathons are intensive design experiences during which teams identify a problem and rapidly develop prototypes of solutions. These events offer a promising venue for studying collaborative design: they are naturalistic, short term, and contained, which mitigates many of the drawbacks of traditional investigations in design research. The objective of this technical brief is to present and describe a transcript dataset of conversations collected from a hackathon team. The dataset includes a transcript of the verbal communication between a four-person hackathon team in the first 2 hours and 9 min of their collaboration. This portion of the design process, totaling 908 segments of speech, details the team’s problem exploration, brainstorming, idea selection, and premature team dissolution. This brief outlines the advantages of having rich transcript data freely available, with a specific focus on new research directions and impact. We include a qualitative analysis of the premature team dissolution, using inductive coding to explore goal misalignment, to provide further context of the collaboration captured in the transcript. We aim for this brief to encourage the use of these data for future investigations of design, teamwork, and hackathon phenomena, as well as act as an exemplar for future publications of open-access datasets.
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 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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".