Experiments and Interventions: Re-envisioning Qualitative Research Methods in Migration
Bibliographic record
Abstract
Abstract The purpose of this chapter on methodology is manifold. It begins by telling the story of the StOries Project, a migration-centred teaching-training initiative that started in 2021 at CERC, in Migration at Toronto Metropolitan University. Using narrative enquiry to delve into the lived experience of migration, employing creative writing formats, was one of its core objectives. Our intention was also to explore the potential of the above conceptual paradigm and methodological process to learn more, and also ‘differently,’ about the migration experience. As the stories- written mostly in a personal and creative non-fiction style- and the academic chapters in this hybrid collection demonstrate, if such experimental projects in migration garner interest from students, early-stage researchers and academics searching for new areas and methods of exploration in this field, pioneering work in cross disciplinary fields could be brought in conversation with each other; and insights from such contributions would help bring new ways of seeing and thinking to migration studies, helping both academic and mainstream readers to ‘know’ about the migration journey more from the perspective of individuals who have experienced it.
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 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 | 0.196 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".