On the Importance of Considering Investigative Practices in Attempting to Understand Operationism
Bibliographic record
Abstract
Grace (2001), by foregrounding the rhetoric surrounding method rather than examining the practices of psychologists in their labs, has provided an incomplete and possibly misleading picture of the complexities of operationism. His suggestion that adopting multiple measures solved one of the critical problems of the operationist position denies the long history of the use of multiple measures, which predated the introduction of operationism by 40 years. This indicates the need to tell a much more complex story than the one offered by Grace. For example, a more accurate analysis must address reasons why the early operationists chose to ignore extant practices of multiple operations. Further, this has to be considered in the context of the introduction of construct validity and logical empiricism in the mid-1950s. Grace's suggestion that multiple operations somehow salvaged the positivist project is therefore problematic.
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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 | 0.105 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.015 | 0.153 |
| Scholarly communication | 0.032 | 0.050 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.012 | 0.025 |
| 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".