Reporting Lines of Inquiry: Documenting Evaluations and Making Values Explicit
Bibliographic record
Abstract
The Program Evaluation Standards call for “rigorous documentation of evaluations” and emphasize evaluators’ responsibility to make explicit the key values that shape evaluations. Clear, complete, values-attentive evaluation reporting is necessary so constituents can understand, learn from, critique, improve, and take action based on evaluations. Yet, scholars have documented gaps in reporting that can limit understanding of evaluations. In this practice note, we provide guidance for reporting a central, value-laden component of evaluations: lines of inquiry. An evaluative line of inquiry is a linked set of an evaluation question, associated criteria or constructs/variables, data collection or analysis method(s), findings, and evaluative conclusion(s) that addresses a specific dimension of quality. We draw on a recent empirical analysis to outline the elements evaluators should include when reporting lines of inquiry and share exemplars that illustrate ways to communicate the links among these elements. We offer this guidance to assist evaluators in documenting their evaluations and making key values explicit to ensure meaningful understanding of evaluations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".