The reconstructed past : reconstructions in the public interpretation of archaeology and history
Bibliographic record
Abstract
Part 1 Foreword Part 2 Chapter 1: Introduction Part 3 Part One: Definitions and History Chapter 4 Chapter 2: Walden Pond and Beyond: The Restoration Archaeology of Roland Wells Robbins Chapter 5 Chapter 3: Colonial Williamsburg: Archaeological Authenticity and Changing Philosophies Chapter 6 Chapter 4: National Park Service Reconstruction Policy and Practice Part 7 Part Two: Measuring Effectiveness for Interpretation and Site Management Chapter 8 Chapter 5: George Washington's Blacksmith Shop Chapter 9 Chapter 6: Castell Henllys Iron Age Fort, Wales Chapter 10 Chapter 7: Ancient Qasrin Synagogue and Village, Golan Region Chapter 11 Chapter 8: The Iroquian Longhouse Chapter 12 Chapter 9: Fort Loudoun, Tennessee Chapter 13 Chapter 10: The Ironbridge Gorge, England Chapter 14 Chapter 11: Fortress of Louisbourg, Canada Chapter 15 Chapter 12: Bent's Old Fort and Fort Union Trading Post Chapter 16 Chapter 13: A Case for Preservation-in-place at Homolovi Ruins State Park, Arizona Part 17 Part Three: Virtual Reconstructions Chapter 18 Chapter 14: Modeling Amarna: Computer Reconstructions of an Egyptian Palace Chapter 19 Chapter 15: From Photo-Realism to Integrated Reconstruction in Buildings Archaeology Part 20 Part Four: The Future of Reconstruction Chapter 21 Chapter 16: The Value of Reconstructions: An Archaeologist's Perspective
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".