ANCESTRAL ENGINEERING: TOWARDS THE INTEGRATION OF ARCHAEOLOGY AND ENGINEERING
Bibliographic record
Abstract
We have long felt those who modern society has named “Engineers” have played a significant role in the evolution of cultures and civilizations. Working with manual tools and the materials that nature provided, historical evidence has proven that practical, innovative, and esthetically beautiful creations emerged from our engineering ancestors. As with most effective research, understanding the past can lead to optimizing the future, and we propose that it is beneficial to study engineering and design in this context. In this paper we will discuss what we have termed “Ancestral Engineering”, and describe the rationale behind the initiative. Two main themes have emerged; engineers helping archaeologists to integrate engineering expertise into their investigations, and archaeologists helping engineers to extract engineering design practice and methodology from other cultures. Within this framework, several initial project ideas are discussed and a suite of Research Questions is proposed. We believe this is an emerging field, with significant opportunity to develop collaborative relationships with interested engineers, archaeologists and anthropologists to pursue discussions and potential research in this field.
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 | 0.032 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.009 | 0.072 |
| Scholarly communication | 0.022 | 0.030 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".