Honoring Lived Experience: Life Histories as a Realist Evaluation Method
Bibliographic record
Abstract
Program participants have been largely excluded as an evidence source in realist evaluations. We test whether and how lived experience as described through life history interviews with pilot program participants can be used as a valid and unique source of data for elucidating context (C)–mechanism (M)–outcome (O) configurations and informing program theory. We use data about “Opening Opportunities,” a program for indigenous adolescent girls in rural Guatemala, to build a theory of change relating to educational attainment. Life histories yield a rich data set that allows probing of quintessential realist questions; capture subtle, hard-to-measure, and longer term contextual factors and mechanisms; elucidate co-occurring CM and MO dyads; help decipher individual- and structural-level contexts; and provide unique additions and refinements to the program theory. Importantly, this work expands potential evidence sources to inform program theory by including the unique insights from those with lived experience.
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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 | 0.073 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".