Bibliographic record
Abstract
Computer scientists and software engineers seldom rely on using experimental methods despite frequent calls to do so. The problem may lie with the shortcomings of traditional experimental methods. We introduce a new form of experimental designs, synthetic designs, which address these shortcomings. Compared with classical experimental designs (between-subjects, within-subjects, and matched-subjects), synthetic designs can offer substantial reductions in sample sizes, cost, time and effort expended, increased statistical power, and fewer threats to validity (internal, external, and statistical conclusion). This new design is a variation of within-subjects design in which each system user serves in only a single treatment condition. System performance scores for all other treatment conditions are derived synthetically without repeated testing of each subject. This design, though not applicable in all situations, can be used in the development and testing of some computer systems provided that user behavior is unaffected by the version of computer system being used. We justify synthetic designs on three grounds: this design has been used successfully in the development of computerized mug shot systems, showing marked advantages over traditional designs; a detailed comparison with traditional designs showing their advantages on 17 of the 18 criteria considered; and an assessment showing these designs satisfy all the requirements of true experiments (albeit in a novel way).
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 | 0.035 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.005 |
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".