A Framework for Epistemological Analysis in Empirical (Laboratory and Field) Studies
Bibliographic record
Abstract
In their search for generalizable behavioral patterns and design principles, cognitive field researchers should reflect on the epistemological limitations of empirical studies. In this paper we describe a framework for epistemological analysis that can help serve this purpose and discuss its application to two prototypical cases of cognitive engineering research: laboratory experiments and field studies. The framework examines two, often implicit, processes in empirical research: the abstraction from empirical data and the substantiation of theoretical constructs and principles. By explicitly considering these two processes in several systematic steps, we can gain appreciation for the epistemological contribution of empirical studies to cognitive engineering research. The framework and its application also provide guidance to such important issues as generalizability of results and external validity. Possible applications of this research include providing guidance to researchers and practitioners in evaluating design principles or conducting field studies.
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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 | 0.258 | 0.199 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.012 |
| Science and technology studies | 0.014 | 0.112 |
| Scholarly communication | 0.028 | 0.033 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".