Three perspectives on Canadian mining: Evolutionary, resource-based, and strategic
Bibliographic record
Abstract
This research, comprised of three essays, considered the evolution of Canada's population of mining firms over much of the 2oth century, the resource characteristics of the firms that survived the industry shakeout, and the strategic positioning of the firms that ranked among the largest mining firms in the world. In the first essay, the forces responsible for the change in the number of Canadian mining firms between 1929 and 1999 were explored using a set of mathematical models designed to identify the underlying dynamic. Density, or the number of mining firms in the population, was found to be responsible for the industry's evolutionary profile. Organizational populations typically experience a significant decrease in membership at one point in their history. This phenomenon, known as a 'shakeout', occurred in the 1980s for the population of Car~adian mining firms. In the second essay, the survival of a cohort of 741 firms that were active in 1969 was tracked over a thirty year period. The firms that survived were no1 only the older firms and the firms with more financial resources but also those firms in possession of a portfolio of resourcebased assets in the form of deposits and mines. In the third essay, the strategic positioning of twenty-six of the world's largest mining firms, seven of which were Canadian, was examined. In an industry where little competitive or corporate strategic variety would be expected, the few firms that chose to position themselves somewhat differently than their competitors were found to outperform those who aligned themselves with the majority of firms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".