Exploring Technology Adoption in Canada’s Mineral Mining Sector: Navigating Through an Interplay of Factors
Bibliographic record
Abstract
The mining sector is currently experiencing a period of disruption where technological \ninnovations such as electric vehicles, artificial intelligence, and drones are transforming how \nthe industry operates. Little is known, however, about the factors that drive, enable, and \nimpede technology adoption in the mining sector, particularly in the context of Canada. To \naddress this gap, this research explores the drivers, enablers, and barriers to technology \nadoption in Canada's mineral mining sector through an online survey, structured by the \ntechnology-organization-environment (TOE) framework, with insights from similar research \nin the context of Australia. The findings of this research suggest that the top three \ntechnologies being adopted by mining companies in Canada are battery electric vehicles \n(BEV's), sensors, and autonomous equipment. In the Canadian context, the technology \nadoption process for mining companies is influenced by a complex interplay of factors \ndetermined by the three commonly cited dimensions of sustainability (economic, social, \nenvironmental). While economic considerations, such as productivity and efficiency, to \nreduce operating costs and competitive pressures underpin technology adoption decisions, \nmining companies are also motivated to adopt technologies by social factors such as \nimprovements to health and safety for workers, and environmental factors such as to reduce \ndiesel emissions. Economic factors, such as costs of the technology, implementation costs, \nlimited internal capital, and the capital intensive nature of the sector, underpin the barriers to \ntechnology adoption for mining companies with operations in Canada. This research \nconcludes with suggestions for future research, and key theoretical contributions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".