SME Annual Meeting Feb. 28-Mar. 2, 2005, Salt Lake City, UT A COLLABORATIVE ROLE FOR A MINING SCHOOL: LIFELONG LEARNING IN THE MINING INDUSTRY
Bibliographic record
Abstract
This contribution examines the motivation and prospects for mining companies to become Learning Organizations and integrate Lifelong Learning into their culture. Mining Schools have a potentially central role to play in developing Learning systems for access by industry. Reference is made to initiatives to develop a system that is based on collaboration between UBC, Edumine and the Canadian Institute of Mining, Metallurgy and Petroleum. Recent experiences with this model are reviewed. Value of a Mining School The health, reputation, image and influence of a Mining School is determined by its value, as well as by the effectiveness of its “marketing”. Value can be judged from three perspectives (University, Industry and Society). The value of a Mining School to Industry can be judged in terms of the strength of its academic support through five pillars (in decreasing order of importance): 1. Educating highly qualified people Industry needs Mining graduates as the next generation of leaders to build and maintain its business operations. They carry the commitment and awareness gained from the accredited education, industrial experience and motivation generated through their degree. The graduate invariably will integrate and lead an interdisciplinary team in the workplace. Schools depend upon industry to maintain consistent support, advice, recruitment and Engineer-in-Training programs. This needs to be more than simply evident in an annual visit with an expectation of being able to meet recruitment needs. Industry Advisory Committees are very useful mechanisms to ensure that more constructive interaction and mutual understanding exists between School and industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.158 | 0.076 |
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".