Theory-based evaluations: Framing the existence of a new theory in evaluation and the rise of the 5th generation
Bibliographic record
Abstract
In this article we defend the idea that theory-based evaluations—contribution analysis, logic analysis, and realist evaluation—are complementary components of a new theory in evaluation. We also posit that we are currently observing the emergence of a fifth generation in evaluation: the explanation generation. Theory-based evaluations have featured prominently in the discourse of evaluators since the mid-1980s. They have developed mainly in response to the need for evaluation of complex interventions. In this article we analyze certain approaches that have matured in their design and application. We use the framework of Shadish et al. to analyze the ontological, epistemological, and methodological foundations of various theory-based approaches in evaluation to appraise their similarities and differences. We observe that all these approaches are grounded in critical realism. Similarities seen in their ontological, epistemological, and methodological positionings, as well as their complementarity in terms of the evaluative questions they address, suggest we may be observing the consolidation of a new theory in evaluation and the emergence of a fifth generation.
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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 | 0.056 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.006 | 0.087 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".