Bibliographic record
Abstract
Life histories are people's histories. They amplify the voices of everyday actors by centering their experiences and interpretation of events. Researchers then carefully weave together these unique experiences with other data sources, including additional interviews, archival and/or documentary data, to construct life histories. Life history research is a useful technique in social movement studies because it develops a version of history that is situated in the lived experiences of those engaging in collective action, emphasizing the role everyday people play in shaping the course of world events. This technique allows researchers to generate rich and textured detail about social processes, understand the intersection between personal narratives and social structures, and focus on individual agency and social context. Life history research captures participant subjectivities while being aware of potential pitfalls such as generalizations and issues of memory recall. This entry examines what life history research is and when it is used. Through an examination of two social movement studies that employ these techniques, we assess the utility of these methods. The entry concludes by discussing potential problems associated with this methodology and offer ways to deal with these issues.
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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.011 | 0.052 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".