A dialectic on validity: Explanation-focused and the many ways of being human
Bibliographic record
Abstract
In line with the journal volume’s theme, this essay considers lessons from the past and visions for the future of test validity. In the first part of the essay, a description of historical trends in test validity since the early 1900s leads to the natural question of whether the discipline has progressed in its definition and description of test validity. There is no single agreed-upon definition of test validity; however, there is a marked coalescing of explanation-centered views at the meta-level. The second part of the essay focuses on the author's development of an explanation-focused view of validity theory with aligned validation methods. The confluence of ideas that motivated and influenced the development of a coherent view of test validity as the explanation for the test score variation and validation is the process of developing and testing the explanation guided by abductive methods and inference to the best explanation. This description also includes a new re-interpretation of true scores in classical test theory afforded by the author’s measure-theoretic mental test theory development—for a particular test-taker, the variation in observed test-taker scores includes measurement error and variation attributable to the different ecological testing settings, which aligns with the explanation-focused view wherein item and test performance are the object of explanatory analyses. The final main section of the essay describes several methodological innovations in explanation-focused validity that are in response to the tensions and changes in assessment in the last 25 years.
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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 | 0.091 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.143 |
| Scholarly communication | 0.025 | 0.036 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".