Dibujos etnográficos y conversaciones migrantes: El mar, los hilos y los retazos que se juntan
Bibliographic record
Abstract
How can one depict the murmur of the sea or the changing tides throughout the day? I encountered this question when Jota sent me recordings of ocean sounds in our WhatsApp chat. He told me he wanted me to imagine the coastal town he had recently moved to, one that reminded him of the beach in Venezuela where he was born and lived until his teenage years. In this article, I reflect on ethonographic encounters with migrants living in Ecuador whom I met during my fieldwork, some of which took place during the COVID-19 pandemic. The conversations we had via WhatsApp once the quarantine was in place, as well as the moments we shared in person before that, became a repository of voices, sounds, and images, which in turn led me to draw specific moments. I thus found myself faced with the challenge of drawing the bonds that connect a group of migrant women —like a vine or crocheted chains— or of finding strokes on paper for a daughter who cares for her mother from afar, in the form of scraps of fabric. The five drawings I discuss in this text have made space to evoke complex and sensitive experiences, while I wonder how they could be expressed in a graphic format (Dix and Kaur, 2019); to situate the relationship between ethnographic drawings and forms of memory that do not always translate into text (Bonanno, 2019); and, above all, to think of ethnography as “a kind of archival effort that connects and deploys affective, material, and temporal fields” (García, 2016).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".