Of Aliens, Money and Politics: Should Foreign Political Donations Be Banned?
Bibliographic record
Abstract
There is significant public disquiet, hostility even, towards foreign political donations. This is evident from some recent controversies, to name but a few, which occurred in 2016. In Canada, Prime Minister Justin Trudeau was criticized for providing ‘cash for access’ to wealthy Canadian-Chinese business communities said to have links to the Chinese Communist Party. The 2016 American Presidential Election saw both sides caught up in controversies involving foreign donations with allegations that the Republican Trump campaign may have acted illegally by soliciting donations from politicians in Australia, Scotland and Iceland, among others, and criticism of Democrat candidate, Hilary Clinton, for foreign donations that were received by the Clinton Foundation. And in Australia, a prominent Australian Labor Party politician, Senator Sam Dastyari, resigned from the Opposition frontbench after it was revealed that he supported China’s position in the South China Sea dispute, in contradiction with his party’s policy, at an event involving an Australian-Chinese donor who had paid his legal bills.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".