What’s Your History? Methodological Prospects and Challenges of Using Life-History Narratives as an Alternative Method to Assess Nonprofits’ Impacts
Bibliographic record
Abstract
Abstract To study the social impacts of third sector organizations seeking the integration of vulnerable populations, we explored an alternative approach: life-history narratives. In this methodological article, we present and detail the steps followed in qualitative research applying this approach. We depict the conceptual and methodological underpinnings of life-history narratives, from the individual narratives to the construction of a typology, in consideration of the broader social context. We also address the challenges encountered in this type of research and then share some of the general results from our specific project to highlight the richness of the approach. Our contribution offers a deep-seated exploration of complex methodologies that involve working with third sector organizations toward concrete alternative means of impact assessment, and the consideration of effects on individual trajectories anchored and situated in more macro, societal context.
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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 | 0.208 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".