Laying the cornerstone of construct validity theory: Herbert Feigl’s influence on early specifications
Bibliographic record
Abstract
Although the theoretical foundations of construct validity theory have been fairly well described, there remains equivocation over what should properly be taken to be its philosophical underpinnings, with some characterizing it as an essentially positivist enterprise, others identifying a realist philosophy underlying the theory, and others still characterizing its foundations as containing elements of both positivist and realist thinking. This paper summarizes recent work representing each of these three different stances on the philosophical foundations of construct validity theory. Explicit connections are drawn between the work of Herbert Feigl—who pioneered a philosophy of science whose roots lay in logical positivism, but which contained notably realist overtones—and early specifications of construct validity theory. Finally, an appeal is made for a realist interpretation of construct validity theory based both on the connections between early articulations of the theory and key Feiglian ideas and also on Cronbach and Meehl’s later reflections on the origins of their influential work.
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How this classification was reachedexpand
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 | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".