Building trust, addressing uncertainty: developing Aboriginal consultation practices for mineral exploration companies
Bibliographic record
Abstract
This thesis examines how mineral exploration companies in the Thunder Bay region are consulting with Aboriginal communities. The research is based on new regulations put forth by the Ontario Ministry of Northern Development and Mines (MNDM) which, as part of a new plans and permits regime, require mining companies to consult with Aboriginal communities prior to any exploration occurring on their traditional lands. Historically, Aboriginal peoples have been left out of resource development decision making, but with increased recognition of Aboriginal and Treaty rights, they have begun demanding prior consultation, and have become influential in natural resource development. For background information and better understanding of the new regulations, interviews were conducted with two representatives from \nthe MNDM. Next, in order to examine what effect these new regulations have had on the mining industry, I interviewed representatives of 15 companies from April 2013 to December 2013. To quantify aspects of this research, this study evaluated companies using Cultural Intelligence \n(CQ) and Dynamic Capabilities (DC) frameworks. My analysis of interview data yielded 21 prominent themes, 7 of which were queried while 14 occurred spontaneously. The most common themes that occurred were ?concerns with government? and ?operational difficulties?. CQ scores ranged from 50% to 89.3% and DC scores ranged from 14.3% to 82.5%. The results show that many companies were already consulting with Aboriginal communities before it became mandatory, but are still facing challenges. The main issues that companies are facing as a result of the regulations are: lengthened project timelines, lack of capacity and resources to properly consult communities, communication with Aboriginal management, unregulated community expenses, uncertainties of role responsibility, and lack of government involvement. I explain the usefulness of the CQ and DC scales in this study and how they are excellent tools for comparing companies that have had successful engagement experiences with those that experience unproductive engagement. I believe that companies are consulting with communities as best \nthey can with the resources they have, but consultation must not be just between company and community; the government must play a stronger role in such proceedings.
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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 | 0.035 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| 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".