What causes mining asset impairments?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When the recorded value of an asset on a mining company's balance sheet exceeds its market value or its value in use, an impairment must be declared. While impairments have been shown to be a common occurrence across mining companies, they also are a major contributor to the industry's low average returns. To better understand the causes and predictors of mining impairments, we collected and analyzed the 266 impairments declared by TSX-listed mining firms between 2002 and 2015. As to the stated reasons for impairments, we find that declines in metal prices account for over half of all impairments, with none of the other eight categories representing more than 13%. To identify factors that might predict future impairments, we drew on the literature on major infrastructure projects. Consistent with this literature, we find that the degree of impairments is higher at mines in developing countries and at mines where the geographic location and mining processes are new to the company operating the mine. Our findings point to several dimensions along which mining firms should seek to improve their forecasting processes.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it