Softly, Softly Catch the Monkey: Innovative Approaches to Measure Socially Sensitive and Complex Issues in Evaluation Research
Bibliographic record
Abstract
Abstract: Many government program evaluations require capture of information that is hard to measure, of a sensitive nature, and difficult for the respondent to articulate. This article suggests research designs and methodologies to assist in overcoming such problems in evaluation research. Our discussion is illustrated by three evaluation case studies. Suggestions for research design focus on increasing reliability through intersubjective certifiability and the use of triangulated respondent groups, as well as varying the composition of the research team at different stages of the research. Methodological suggestions are for multifaceted research processes, run in parallel and in sequence, to uncover topics on which findings vary and to find information “hidden” in other approaches. Methods for improving recruitment and retention of respondents are also discussed. We conclude by critically evaluating the outcomes of applying these new approaches and discussing the implications of gaining different or new information from adopting such innovative approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.375 | 0.470 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".