Luck of the draw: Risk considerations, management responses, and policy implications for archaeological chance finds in British Columbia, Canada
Bibliographic record
Abstract
Unanticipated discoveries of objects and features of archaeological interest occur for various reasons and in diverse contexts coincident with activities that alter land surfaces. When community development and resource extraction projects unexpectedly encounter a chance find, heritage resources management efforts are required. Such efforts necessarily expose project proponents to financial and regulatory obligations and risk that may or may not be balanced out by gains from additional engagements with stakeholders and further studies by archaeologists. British Columbia’s archaeological record and applicable resource management policy provide an apt case study for understanding risk, policy, and management implications for archaeological chance finds. A typology for archaeological chance finds enables analyses that indicate there are new opportunities available to manage risk. The typology allows for consideration of alternative approaches that draw from international best practice. A suggested process improvement seeks to offset adverse effects to archaeological resources through overcompensation. Recommendations to align policy and practice are provided. These include the implementation of measures to improve triggering mechanisms for archaeological assessment and changes to established assessment processes for chance finds from the perspectives of regulators, proponents, practitioners, and Indigenous Nations.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".