The Language of Evaluation Theory: Insights Gained from an Empirical Study of Evaluation Theory and Practice
Bibliographic record
Abstract
Abstract: Broad concern for language issues in evaluation has been limited in comparison to other social science disciplines. In this article, some occasions of definitional or conceptual confusion with evaluation theory language are identified that emerged during a study conducted by Christie. We suggest that much of the language we use to describe evaluation practice is steeped in theoretical terminology, which may limit the utility of the language. We also argue that theoretical language ought to be used with great care, with attention to the subtleties and nuances of terms, for there may be unexpected confusion or ambiguity in the field about the terms we routinely use. A research agenda is offered, suggesting that it would be both an informative as well as a useful task for us to learn more about the everyday “folk theories” of the field and the vernacular used to describe them.
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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 | 0.159 | 0.348 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.007 | 0.036 |
| Scholarly communication | 0.019 | 0.031 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".