Timelines, convoy circles, and ecomaps: Positing diagramming as a salient tool for qualitative data collection in research with forced migrants
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
Visual elicitation methods, such as diagramming, are growing in their use with vulnerable populations, trauma-informed research, and social work studies where the use of traditional oral interviews alone may be lacking in their ability to increase access to different areas of human consciousness. The adoption and designing of innovative diagramming and visual methods have the potential to push the boundaries of data collection in understanding the experiences of forced migrants. However, scholars have seldom adopted this method in forced migration research. In this article, the authors explore three diagramming methods-timelines, convoy circles, and ecomaps-to highlight the possibilities of their use for social work research with forced migrants. The benefits of utilising these methods in support of the unique characteristics and challenges of forced migrants are also discussed. The article concludes by identifying several limitations while advocating for the adoption and documentation of the use of diagramming in studies with forced migrants.
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 | 0.055 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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