Bibliographic record
Abstract
After many years during which indigenous laws were mostly absent from narratives of Latin American law, presently, legal historians wish to integrate them. However, to do so requires answering the question of what we know about indigenous laws and how we can approach them. Writing the history of indigenous laws from precolonial times is especially challenging not only because of the diversity of human groups that occupied the continent, but also because of the disparity of available sources, ranging from material vestiges and pictographic documents to texts produced in indigenous writing systems. Furthermore, the colonial period has left us with a wide range of alphabetic texts, diverse in authorship, languages, formats, degree of accuracy, and sources selected, that describe precolonial law. Indigenous peoples, mestizos, and Spaniards also wrote historical narratives and accounts of deeds and services; furthermore, they participated as litigants in lawsuits in which they expressed their vision of law and justice. What does this evidence tell us about precolonial normative orders and the way in which they intersected with colonial law after the Iberian imperial conquests? To answer this question, this chapter proposes an interdisciplinary approach, surveying what has been done, and what could still be done.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".