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Record W2142156492

Productivity Trends in the Coal Mining Industry in Canada

2004· preprint· en· W2142156492 on OpenAlexaboutno aff
Jeremy Smith

Bibliographic record

VenueRePEc: Research Papers in Economics · 2004
Typepreprint
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityGold miningEconomicsMining industryNatural resource economicsAgricultural economicsBusinessEngineeringEconomic growthMining engineering
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this report is to uncover the factors behind what has been a very strong productivity performance from the coal mining industry in Canada over the past four decades. It is found that real price movements have had a substantial impact on productivity growth in the coal mining industry in Canada. The real price of coal increased sharply in the 1970s due to higher demand caused by the oil price shock. This increased the profitability of sites of marginal quality and thereby lead to operations on less productive sites than those in production at that point. This had the effect of lowering the average productivity of the overall industry. However, since the 1970s, the real price of coal has fallen steadily, reversing this effect and hence contributing to the high productivity growth of the 1980s and 1990s. Another factor in this impressive productivity performance, at least in the 1980s, was the gradual closing of underground coal mines and the concentration of production on open surface mines. Surface mines typically have higher levels of labour productivity than underground mines, so this effect reinforced the price effect in increasing the average productivity of the industry. The 1990s saw the computerization of several stages of the production process, from site planning to extraction. Despite having the image of an old-fashioned industry, the coal mining industry in Canada is actually among the most intensive users of advanced technologies, and this certainly appears to have contributed to the industry’s strong productivity performance as well.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.284
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2004
Admission routes1
Has abstractyes

Explore more

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