Bibliographic record
Abstract
Abstract The first of the book’s chapters to evaluate the myriad investigations into terrorist informers, this chapter first identifies the available forms of investigation. Broadly speaking, these investigations have one of two aims: to discover evidence of (suspected) offences, or to discover the truth. There is naturally some overlap between the two, as criminal justice proceedings reveal information about what happened, thereby contributing to truth-finding. Yet in any given case decisions have to be made regarding whether to prioritise a truth-finding investigation or criminal proceedings. The chapter then assesses Canadian Judge Peter Cory’s investigations into cases allegedly involving ‘collusion’. Informers were allegedly directly involved in five of the eight murders Cory investigated. The UK-based public inquiries that followed from Cory’s recommendations are also examined in this chapter, chiefly in respect of to what extent truth-finding or criminal justice were prioritised, and the influence of secrecy surrounding intelligence and informers upon these investigations.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.088 | 0.033 |
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".