Relationship between Resources and Value Creation in the Kenyan-owed Mining Enterprises in Kenya
Bibliographic record
Abstract
Studies show that countries such as United States of America, Canada, Australia, Chile, Ghana and South Africa use the right machinery and equipment to extract minerals which when sold contribute significantly to the country’s GDP. Kenyan mining industry contributed Ksh.15023 million only to the GDP in the second quarter of 2020. Therefore, it was important to establish whether Kenyan owned mining enterprises have adequate resources. The objective of this study was to establish whether resources have a relationship with value creation. This study was a cross sectional survey. A questionnaire was used to collect data from Kenyan owned mining enterprises, where both semi-structured and open-ended questions were used. A quantitative approach was employed in data analysis. Results of the research demonstrated that there was correlation between resources and value creation. ANOVA results show that resources and value creation association were statistically significant. However, Kenyan owned enterprises do not have adequate resources. This study found out that most of the Kenyan owned mining enterprises do not have adequate resources to facilitate value creation processes. These resources are crucial in mining industry because finances are used to acquire all other resources, such as machines and equipment resources that are used to extract minerals throughout the value chain process in the mining industry. Future studies can be undertaken to establish whether other specific types of mining-related enterprises have resources to facilitate mining process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".