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 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 | 0.137 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.005 | 0.089 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.022 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".