Honoring Lived Experience: Life Histories as a Realist Evaluation Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.045 | 0.020 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it