All that is Gold Does Not Glitter: Canadian Foreign Direct Investment and Gold Extraction in the Middle Cauca Gold Belt, Colombia
Bibliographic record
Abstract
Foreign Direct Investment (FDI) can contribute to economic development not only by being a source of foreign capital, but also by providing employment opportunities, increasing competition, and most importantly transferring skills and knowledge i.e. spillover effects. However, these positive effects are not automatic for host countries and depend largely on the policies in place. Taking a closer look at Colombia, this capstone project examines policies used to attract FDI to Colombia, a country that has experienced over six decades of civil conflict, numerous human rights abuses and painful internal displacements. It posits the following questions: in war economies, are voluntary policies and tools enough to mitigate the negative spillover effects FDI has had in Colombia? Do voluntary principles guide corporations and ensure they do not violate human rights? It is found that FDI has contributed positively to Colombia’s economic development but more needs to be done in order for Colombia to fully benefit from FDI. The first section provides a general introduction to the paper and provides information on objectives, methodology and limitations of the study. The second section provides a brief overview of the rise of FDI in the wake of Colombia’s civil conflict. It will offer a snapshot of current policies and regulatory frameworks and highlight areas of institutional weakness in Colombia. Part Three provides a synthesis of key policies and instruments designed to hold Canadian extractive companies to account. The section then moves from a critical analysis of the instruments to examining whether these instruments are effective in the particular context of the Middle Cauca Gold Belt. The fourth section undertakes a case study in the Middle Cauca Gold Belt: Gran Colombia Gold to understand how policies and instruments are being implemented in practice and to identify implementation gaps. Part five explores evidence from other jurisdictions and four policy options. Finally, part six provides conclusions and concrete recommendations for how to strengthen the current framework and approach.
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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 | 0.001 |
| 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".