Organizational Changes and Employment Shifts in the Mining Industry: Toward a New Understanding of Resource-Based Economies in Peripheral Areas
Bibliographic record
Abstract
Technological development and innovation have improved productivity in mining; consequently, mining employment has decreased. In the light of a growing demand for raw materials, mining activities have increased in many mining regions. In Sweden, this development has had modest effects on employment levels, although improved productivity is not the sole explanation. The aim of this paper is to examine the extent to which a reorganization of mining employment has occurred; which industrial sectors might be affected by such restructuring; and whether restructuring has any implications on gender equality. Previous studies by Eriksson (2004) and Knobblock & Pettersson (2010), have demonstrated that new employment strategies, in addition to mergers and acquisitions, have been implemented and that they affect employment. However, the extent of this reorganization remains unknown. The current study was carried out in Vasterbotten, a mining region in northern Sweden. By means of unique longitudinal database, connecting information concerning individuals and companies and covering the total population of Sweden, along with data from the Mining Inspectorate of Sweden, this study examined issues of employment, labour mobility, and gender equality. Key informant interviews with mining stakeholders were also conducted, adding to the knowledge concerning organizational changes. The data suggests that an organizational change had occurred, with jobs having been outsourced by the mining industry to other sectors. This led to the start-up of new companies, increased employment, and the inclusion of more women in the field. Results also indicated that the mining industry and related companies have represented new development potential in peripheral mining regions. Keywords: Mining, Employment, Outsourcing, Innovation, Regional development
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".