Bibliographic record
Abstract
Denmark is currently going through a full-blown intelligence scandal. It includes charges of illegal activity lodged by the Danish Intelligence Oversight Board (TET) against the Danish foreign intelligence service (FE), as well as a range of criminal cases brought against the former head of FE, a former minister of defence, and a former intelligence officer on charges of leaking classified information. In this post, I argue that these scandals can best be understood through the lens of a series of obstinate choices made by the Danish government and its representatives. Seemingly, because key decision-makers lacked trust and got fed up with leaks, the situation was handled aggressively from the start, as a matter of principle. I explain the complex scandal but focus on specifics only in the case against former minister of defence, Claus Hjort Frederiksen, as his case is the most clear-cut and observable for outsiders.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.036 |
| Scholarly communication | 0.034 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".