Kinesiske råstofinvesteringer i Australien og Canada - erfaringer for Danmark og Grønland
Bibliographic record
Abstract
Kinesiske investeringer har udviklet sig til et skræmmebillede i debatten om udnyttelsen af det grønlandske råstofpotentiale, men er der grund til bekymring? Artiklen ser nærmere på erfaringerne fra Australien og Canada, som begge har været genstand for omfattende kinesiske råstofinvesteringer det seneste årti. Begge lande har haft overvejende gode erfaringer, selvom der har været tale om vanskelige forløb præget af folkelig skepsis og gensidige misforståelser, og hvor både de kinesiske selskaber og værtslandenes myndigheder har gennemgået en stejl læringskurve. Danmark og Grønland kan med fordel tage ved lære af de australske og canadiske erfaringer, som viser, at nøglen til succes er dialog og stram regulering, som har tvunget de kinesiske investorer til at lære og tilpasse sig. Artiklen argumenterer også for værdien af et institutionelt perspektiv, herunder spørgsmålet om organisatorisk læring, i studiet af kinesiske investeringer i udlandet.
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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.052 | 0.007 |
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".