Bibliographic record
Abstract
The increasing availability of web based collaboration tools fuels design conversation between heterogeneous stakeholders across organizational boundaries, underscoring the need for new designers to get to the heart of conversations that might include huge numbers of entries. The goal of this work is to show that linkography is a viable candidate to help make that kind of discovery possible. A linkograph links design moves with prior moves, resulting in a model of the design episode. The research methods were mixed, though primarily qualitative. The primary data comprised records of a series of eleven two to three hour design meetings over a six month duration, with five participants. A model for predicting the location of topic shifts was developed on the first two exploratory meetings, and tested on the remaining nine design meetings. The model used a finer-than-topic-shift granularity linkograph of the nine meetings to predict topic shifts. It combined a measure of both backward and forward links, plus a threshold, in order to segment the design discourse on topic shifts. An additional threshold comprising a number of segments was used to filter transitive links to retrieve contextualizing information from the discourse. The test included quantitative comparison of model segmentation with human segmentation, and qualitative evaluation of relevant contextual information drawn (using the model, the segmentation, and the linkograph) from previous design conversations. The results suggest that employment of linkography is a viable and pragmatic addition to design rationale.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.011 |
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".