Bibliographic record
Abstract
As I write, a diesel-powered coal-train rumbles through the heart of my city only a few blocks away, its entire load bound for Japan.British Columbia remains a major producer of coal, but, ironically, the smell of burning coal is as unlikely here as the sight of dray-horses.In Canada it is an endangered scent preserved almost exclusively at living-museum blacksmith forges, an unmistakable odour that I suspect few of my neighbours would recognize.Nor could they easily conceive of working lives spent deep in the bowels of the earth, wrestling with slabs of coal in a near-total blackness, counting on a truce with the forces of combustion and gravity in order to survive to the end of another shift.Enduring six months at sea for the privilege of living in a kind of exile on the edge of the world, for the sake of earning a few shillings more each month in especially dangerous mines, this too is now almost beyond belief.This study began in an effort to come to grips with that almost unimaginable past.Mining history is a rich vein, but combining it with immigration -not to mention emigration -history constitutes something rather different.By the late twentieth century, British Columbia had become such an attractive place to live and work that many in the region had lost sight of the fact that it was something of a hardship posting in the nineteenth century.So I started with the question of why anyone would emigrate from the burgeoning British coalfields and head for the industrial and imperial backwater of Vancouver Island.Doing so took me deeper into the literature on coalmining and colliery society in Britain until finally I resurfaced on my own side of the
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.578 | 0.360 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".