Bibliographic record
Abstract
Today approximately 6 million people who live in the area explored in this book, but also in a diaspora that includes places like Dallas, Texas, and Vancouver, British Columbia, speak one of the many Mayan languages. Learning Mayan at home is a key component of what it means to be a Maya person in the 21st century, as Maya culture is no longer centered only on the maize agricultural system and dynastic kingship we discovered in earlier chapters. Now Maya people create hip-hop, practice law, win the Nobel prize, and also continue to farm small-scale maize fields where they plant corn, beans, and squash as did their ancestors. They live in the large cities of modern Guatemala and in small villages high in the remote mountain ranges of Belize. They do not agree on what it means to be Maya, a term that originated in the colonial period 1 but was not embraced by people in the area until much later. They do not agree if the name “Maya” is even meaningful to all of them in the same way, 2 other than describing their language family. However, from an outside perspective, Maya culture has both transformed and survived and is an example of one of the most resilient cultures known to scholars of history and culture. But how did we go from royal palaces to hip-hop? What happened between the 9th and 21st centuries? How do Maya people today understand their glorious ancient past – the queens, hieroglyphic panels, and masterpieces of art? What parts of the ancient daily lives we have just discussed are still salient to Maya people today?
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.282 | 0.127 |
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".