Thinking About Measures and Measurement in Positivist Research: A Proposal for Refocusing on Fundamentals
Bibliographic record
Abstract
We challenge two taken-for-granted assumptions about measurement in positivist research. The first assumption is that measures and measurements are relevant for quantitative, but not qualitative, research. We explain why they apply to both types of research. The second assumption we challenge is that existing measurement practices are unproblematic, even if researchers sometimes vary in how well they enact them. We explain why current norms (both espoused and enacted) are deficient in some important ways because they fail to emphasize the fundamental issues of measures and measurements. Drawing on symbolic logic, we provide a framework to help positivist researchers to assess efforts in measuring and measurement regardless of their quantitative or qualitative orientation. The framework provides more parsimonious and broadly applicable guidance than available to date and suggests the need to refocus on measurement fundamentals. The online appendix is available at https://doi.org/10.1287/isre.2017.0704 .
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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 | 0.350 | 0.347 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.009 | 0.139 |
| Scholarly communication | 0.027 | 0.079 |
| Open science | 0.013 | 0.016 |
| Research integrity | 0.016 | 0.041 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".