Methodological Issues in the Evaluation of System Analysis and Design Techniques
Bibliographic record
Abstract
This chapter examines methodological issues arising in the comparison of systems analysis and design techniques. An argument is made to establish a foundation of research and more broadly consider the management of scope in analysis and design research. A discussion of why and how we evaluate techniques is provided. A generalized approach combining both deductive and inductive reasoning is presented and a combined grammar-based and cognitive-based approach to comparison is discussed. In addition, concepts from Friedman’s economic methodology are applied in the choice between alternative ontologies that underlie grammar-based comparisons. The chapter concludes with a set of nine questions that researchers should consider when designing and developing research in the evaluation of systems analysis and design techniques.
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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 | 0.474 | 0.588 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.030 | 0.024 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".