Indigenous Maya Knowledge and the Possibility of Decolonizing Education in Guatemala
Bibliographic record
Abstract
Maya peoples in Guatemala continue to practice their Indigenous knowledge in spite of the violence experienced since the Spanish invasion in 1524. From 1991 until 1996, the state and civil society signed a series of Peace Accords that promised to better meet the needs of the Maya, Xinka, Garífuna and non-Indigenous groups living there. In this context, how does the current educational system meet the varied needs of these groups? My research investigates the philosophy and praxis of Maya Indigenous knowledge (MIK) in broadly defined educational contexts through the stories of 17 diverse Maya professional women and men involved in educational reform that currently live and work in Guatemala City. How do they reclaim and apply their ancestral knowledge daily? What possible applications of MIK can transform society? The findings reveal that MIK promotes social change and healing within and outside institutionalized educational spaces and argues that academia needs to make room for Indigenous theorizing mainly in areas of education, gender, knowledge production, and nation building. I analyze these areas from anticolonial and critical Indigenous standpoints from which gender and Indigenous identities weave through the text. Thus, I rely on Maya concepts and units of analyses (Jun Winaq’) guided by an Indigenous research methodology (Tree of Life) to conduct informal and in-depth interviews that lasted 2 to 4 hours. In addition, I held a talking circle with half of the participants. My analysis is founded on my own experience as an Indigenous person, my observations and participation in two Maya organizations in 2007 and a review of secondary literature in situ. \nThe study contributes to a general understanding of contemporary Maya peoples and knowledge, and describes the theoretical validity of the Maya concept of Jun Winaq’. I argue that this concept seeks to heal individuals and a society to strengthen the Maya and all peoples. Throughout the dissertation I highlight the value of Indigenous knowledge and voices as parts of a political process that has the potential to decolonize mainstream education. I end with a graphic illustration of the elements in Maya Indigenous education and discuss future research for building a political agenda based on self-determination and healing relevant to Indigenous struggles globally.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 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".