Proceedings of the Third International Workshop on Conducting Empirical Studies in Industry
Bibliographic record
Abstract
The CESI series of workshops was born out of the need to shift the focus away from simply conducting empirical studies (be them case studies, experiments, surveys, etc.), and reporting their results, to putting them firmly in the context of the software industry. In other words, the aim was to better understand the challenges and opportunities brought about by the organisational context in the conduct of empirical studies. There were several reasons for this shift. Simply knowing empirical procedures (from the literature or by conducting studies in, often tamed, academic environments) didn't seem to prepare one for how to plan and conduct empirical studies in industry. There are just too many hurdles in the way of conducting successful studies in industry. Examples are: (i) understanding specific problems in practice such that conducting relevant studies would give some insight into solving observed problems; (ii) ploughing through organisational politics to zero down to key investigative questions and associated measurable variables; (iii) balancing between scientific purity in empirical procedures and being practical enough to yield usable results for making business decisions within short cycle-times; (iv) taking the results of studies and putting them into practice, in retrospect, to validate the conduct and the outcome of the studies; and more.
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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 | 0.262 | 0.215 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".