Glocalization and Transnationalization in (neo)-Mayanization Processes: Ethnographic Case Studies from Mexico and Guatemala
Bibliographic record
Abstract
In this article, the author focuses on the field of neo-Mayanity and its current transformations. She analyzes these transformations using a historico-ethnographic approach, which includes two phases. The first one consists in reconstructing the historical development of the “Mayan” category in two different social contexts. The second one focuses on current narrative and imageries produced around this category, stemming from ethnographic fieldwork in Mexico and Guatemala. Since the “2012 phenomenon”, in both countries, the accelerating transnationalization of the religious leaders has triggered a resignification of contents through various logics of rearrangement, innovation, cohabitation and glocalization. Finally, she demonstrates that the variations in the different ethnographies are linked with the religious leaders’ biographies and the modes of signification of the “Mayan” category—influenced by the socio-historical contexts of production.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".