Bibliographic record
Abstract
A seminal study by McKinsey and Co. in 1998 examined the issue of the impending shortage of talent in U.S. and global businesses. They predicted that significant resources would need to be invested in the recruitment, training, and retention of the human capital that provides competitive advantage to firms. For those of us in the resource sector with enough seniority or enough courage to reflect on the state of the world in 1998, pretty much the last thing on our minds would be where to find sufficient talent to sustain and grow our business. This time was the beginning of a long downturn in the industry where few new recruits entered the business, and many experienced people were forced out. A 1999 exit survey of graduates from U.S. universities offering programs in geology or mining carried out by the American Geological Institute suggested that fewer than 200 new graduates in the entire country planned to work in the mineral exploration or mining business.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.017 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".