Changing our view on design evaluation meetings methodology: a study of software technical review meetings
Bibliographic record
Abstract
By contrast to design meetings, design evaluation meetings (DEMs) have generally been considered as situations in which, according to DEMs methodologies, design activities are quite marginal. In a study of DEMs in software development, i.e. in technical review meetings following a particular review methodology, we showed: (i) the occurrence of design activities as part of an argumentation process; (ii) the relative importance of cognitive synchronisation as a prerequisite for evaluation; (iii) the important role played in evaluation by argumentation that makes explicit the underlying design rationale (DR). On the basis of our results, we discuss the potential for using DR methodologies in this kind of meetings.
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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 | 0.155 | 0.363 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".