Autonomy among Indigenous women in Rural Colombia: “free to be, think, and act in our territory”
Bibliographic record
Abstract
There is limited qualitative research to support the use of the most common conceptualizations and operationalizations of women’s autonomy, especially in the Latin American context and even more so for Indigenous populations. This study uses photovoice, a photography-based Participatory Action Research method, to conduct a qualitative assessment of how female Indigenous smallholding farmers from Nariño, Colombia, define women’s autonomy and which factors facilitate and hinder their autonomy. Results show that women felt autonomous when: a) they were free to make decisions important to them and to express themselves; b) they had opportunities to be economically independent doing work they valued; and c) their cultural and collective autonomy was effectively protected. Significant barriers to autonomy included issues related to colonization, the devaluation of women’s work, machismo culture, limited access to education (traditional and formal), and unjust employment opportunities. The use of Photovoice proved to be a valuable qualitative approach for studying this particular group by empowering participating Indigenous women to share their experiences, perspectives, and knowledge. The results from this study can inform local policies and programs, improve the interpretation of quantitative results from similar contexts, and facilitate the development of quantitative tools to measure women’s autonomy more effectively.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".