Writing the Roots. A Reflection on Migration, Gender and Environment through Arts
Bibliographic record
Abstract
The contributions in this series explore the migration experience using different kinds of “data.” Our contributors use works of art, novels, songs, and movies to explore many of the same questions they generally ask in their social scientific research. What additional insights come from using arts and culture to think through the issues that concern us? What can images and notes reveal that scholarly work cannot? We also invite original stories, poems, photo essays or art works. Ideas for contributions are wholeheartedly invited at any time. Please contact Marie Godin or Peggy Levitt. For this essay we want to share one of the many educational and artistic processes undertaken within the framework of the Participatory Action Research project “Migrantas en Reconquista” (Migrant women of the Reconquista River) that was undertaken by the National University of San Martin and the International Development Research Center (IDRC) Canada between 2019 and 2022. The project involved an interdisciplinary network of researchers, students, migrant women, and community leaders. Its goal was to assess the unequal effects of climate change on migrant women and to strengthen the community’s strategies of adaptation to socio-environmental change with a gender focus in mind. In this essay, through the photography of Teresa Perez, a visual artist and teacher, who was at the center of the project’s partnership with migrant women using art, we reflect on our collaboration in the creation of a book of chronicles of medicinal herbs (“Pohã Ñana” in Guaraní language) that rural migrant women use for different physical and emotional ailments (Fig. 1). Creating this book was a way for them to “produce memories” about their lives in the countryside and to link them to their urban present.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| 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".