A Framework for Epistemological Analysis in Empirical (Laboratory and Field) Studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it