The Preservation and Stewardship of Archaeological Sites in the Boreal Forest: A Public Issues Approach
Bibliographic record
Abstract
Archaeological sites in the boreal forest are facing threats due to urban development, resource exploitation, vandalism, and infrastructure development, among others. In the context of archaeological site preservation as a public issue, I examine the perspectives of various publics towards the preservation and stewardship of archaeological sites in the boreal forest. Through a series of interviews, I examine the opinions of three publics involved in the archaeological process in Ontario: developers, First Nations, and archaeologists. I outline the participants’ opinions on the meaning and goals of preservation, the preservation of non-physical aspects of sites, such as oral history and site spirituality, preservation methods, site ownership and access, land use and development, involvement in the archaeological process, and funding. I also identify common themes which presented themselves throughout the interview process, such as the importance of education; the necessity for communication, collaboration, and cooperation; the problem of artifact curation; the perceived lack of genuine government involvement; and the publication of cultural resource management (CRM) archaeology’s “grey literature”. \n \n Finally, I present suggestions on the preservation of archaeological sites which take into account the participants’ perspectives uncovered during the interview process. I conclude that preserving archaeological sites can be done using three techniques: education; communication, collaboration, and compromise; and using one of three general methods to preserve sites and artifacts. Education can be used to create public issues, teach people about the importance of archaeology and archaeological sites, and teach the involved publics about the goals and methods of CRM archaeology in Ontario. Encouraging communication, collaboration, and compromise between the interested publics includes the perspectives of formerly neglected parties, builds relationships between publics, and creates newly vested interests in site preservation. Three methods to preserve archaeological sites include site stabilization and monitoring, allowing sites \nto decay naturally, and excavating sites and curating the artifacts and oral histories for the long-term.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.024 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".