Heritage policy landscapes of Nunatsiavut: approaching the development of historic resource management policy and law in Northern Labrador
Bibliographic record
Abstract
As a result of the Labrador Inuit Land Claims Agreement (being schedule to SNL 2004, c L-3.1) and the Nunatsiavut Government Organization Order (NGSL-2019-07) the Nunatsiavut Government (NG) has the power to develop and implement heritage policy and law. An approach to the development of recommendations for heritage policy and law in the region involving critical use of the Nunatsiavut Government’s policy cycle and public engagement is outlined in this dissertation. The research presented here was conducted through the lens of landscape archaeology which can accommodate multiple perspectives, and which can help bridge theoretical and ontological divides. Relevant discussions that took place during annual regional heritage forums from 2010-2018, and during three public engagement tours on heritage that took place between 2017 and 2019 were thematically reviewed using qualitative data analysis software. The results were then compared to the results of a review of international heritage agreements and Canadian provincial and territorial heritage laws. This allowed for the development of recommendations for both legislative and non-legislative policy measures that the NG can consider as it works towards passing its own heritage law, and as it continues to develop related policy in accordance with the Agreement, and the NG Organization Order. The idea that policy work aimed at effectively managing historic resources has the potential to create societal opportunities beyond the heritage domain was also explored.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".