'Mining the Rock': towards a benefit-sharing framework for human genetic research in Newfoundland and Labrador
Bibliographic record
Abstract
For decades the province of Newfoundland and Labrador (NL) has been a hot spot for genetic research, largely due to the presence of a genetic founder population. At the same time, the province is well-known for its abundance of natural resources, including the historical fishery and the more contemporary mining and oil and gas sectors. Beginning in the early 2000s, several individuals began referring to the province’s unique genetic architecture as a “resource” – language that resonates particularly well in a province whose economic outlook is intimately tied to natural resource development. This dissertation explores whether the descriptor of “resource” is aptly suited to define the genetics of the province’s residents and if so, how this new resource can be most appropriately harnessed and developed to truly benefit those residing in Newfoundland and Labrador. As a result, this study provides an exploration and discussion of the comparison between genetic data and the more traditional natural resources. It is clear that genes are not ore and their development will require additional considerations. However, the language employed in natural resource development, community development, and economics provides a valuable starting point for this discussion and is the essential thread that runs throughout this dissertation.
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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 | 0.065 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.053 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".