Annotating voice in design research
Bibliographic record
Abstract
Design research processes are complex, and it is difficult to open them to others.As design researchers, we are often challenged with communicating the nuances behind these processes in ways that highlight what might be meaningful and transferable to others.Further, there are often specific lenses that we use to analyse and cut across our work, but these cuts are often difficult to convey.This Work in Progress presents an analytical approach to investigate a design research process through the lens of voice.This uses annotations to investigate voice in a set of design research artefacts, such as sketches and embroidery pieces.Re-examining these artefacts through this specific lens allowed us to reveal deeper layers of information, uncovering richer and more nuanced insights.The result is an analysis that foregrounds voice in design research, which we propose is a way to consider the complexities of such processes in greater depth.
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 | 0.068 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".