Bibliographic record
Abstract
This article discusses the role and contributions of Aboriginal women to mining negotiations and project development in Canada. Four interviews were conducted in the summer of 2014 as part of my MA thesis in Native Studies at the University of Manitoba. The results of these interviews are shared so that they may shed light for on-going experiences of Aboriginal communities with this significant industry. Participants are not named, and aliases are used according to their direction. Mary Jane and Hazel are the Joint Venture and Impact and Benefit Agreement (IBA) Coordinators for an Aboriginal community actively involved in mining. Together, they have negotiated numerous agreements for mining that were already happening on and near their traditional lands, as well as future anticipated mining projects. Marion is the elected Vice-President of an economic development enterprise representing several Aboriginal communities; she is also the Vice-President of a joint venture with a mining contracting company. Dianne is the Indigenous Community Relations Manager for a local operation of one of the largest mining companies in the world. Her responsibilities include coordinating relationships with surrounding First Nation communities, engagement initiatives for mine employees and all community members. Dianne also leads a team that coordinates cultural reclamation initiatives alongside land restoration of reclaimed mine sites.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.053 | 0.020 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".