Keeping Up with the Sun: Revitalizing Isthmus Zapotec and Ancestral Practices through Cellphilms
Bibliographic record
Abstract
This article describes how cellphone technology, specifically cellphilms – films of varying lengths made with cellphones by everyday people – are being taken up in the community of Union Hidalgo as a platform to foster an intergenerational dialogue between youth and Elders, with the specific goal of preserving the Zapotec language and ancestral practices. The study is rooted in two distinct but complementary methodologies. The first is an Indigenous Oaxacan methodology or practice known as comunalidad, a process rooted in a commitment to strengthening the future of communal lifeways. These lifeways reflect the values of the Zapotec ancestors, the community, and local Indigenous identity by asserting strategies that engage a cultural praxis that does not perceive education in terms of formal classroom curriculum. Rather, they engage the cultural process of learning, whether it is making tamales, planting corn crops, or “doing” other embodied ancestral Zapotec practices. This comunalidad methodology is merged with a participatory cellphilm method, in the form of a series of workshops adapting cellphone video, Web, and social-media technologies. The results included a dialogue about how new technologies can inform the transfer of Indigenous ancestral knowledge, language, and embodied practices in the twenty-first century.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".