When the Archaeologists Leave
Bibliographic record
Abstract
Abstract The Hacienda El Progreso functioned as an important Ecuadorian agro-industrial enterprise in the late nineteenth century. Operating out of San Cristóbal Island in the Galápagos archipelago, the plantation exported refined sugar, coffee, cattle products, and other goods to national and international markets. From its beginnings in the 1860s, the plantation established the first permanent human settlement on the island, and long after its demise in the 1930s, it continues to exert an important influence in local culture. Contemporary communities of San Cristóbal are shaping their identities based on the historical importance of the hacienda. The summer of 2018 was our last field season. From its start, the Historical Ecology of the Galápagos Islands project involved close participation with communal authorities and town leaders to investigate the island's human past. In this article, we discuss legacy and services of our project in the contemporary setting of Galápagos. We examine the relevance and contributions of our project to education, heritage policies, and the local economy. We discuss lessons learned from interactions and collaborations between archaeologists and the local community, and we evaluate the consequences of implementing an archaeological project on a remote environmental sanctuary where interest in human history can collide with the agendas of nature conservation and a lucrative ecotourism industry.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.044 | 0.011 |
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".