Tracing Research Through Design with Ponte: bridging game development repositories and qualitative research tools
Bibliographic record
Abstract
As game design research is an emerging area of games research, game design researchers are still establishing consensus around baseline methodological practices that enable us to stop talking past each other and start putting our research efforts into genuine conversation. Stemming from our work on game design research methodology, we are proposing an interstitial design research step between design process data collection and design data analysis: corpus assemblage. In this paper, we overview the Method for Design Materialisation (MDM) and its workflows, then zoom in on what existing research approaches (and their supporting instruments) leave unaddressed in the context of game design research. We then introduce Ponte, a design-oriented research tool that bridges the material, technical, and methodological space that exists between design process-oriented data collection and design process-oriented data analysis. Ponte makes visible metadata, a commonly invisible layer of process-related information consistently being tracked by our digital work environments. Through the exploration and organisation of design metadata, Ponte invites design researchers to intentionally and explicitly undertake corpus assemblage as a crucial stage of data engagement that magnifies our abilities to make sense of design materials, processes, and practices.
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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 | 0.133 | 0.277 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.006 | 0.034 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".