Bibliographic record
Abstract
Digital information has undeniably become vital to the work of intelligence agencies. Most agencies now routinely collect and analyze data from myriad facets of an individual’s social and relational life. However, how this is done varies and depends very much on an agent’s seniority in their profession, their capacity in terms of human, financial, and technological resources, and, most importantly, their vision of what intelligence is as an activity. Based on a study of the principal intelligence agencies of nine Western countries (the United States, the UK, Canada, Australia, New Zealand, France, Germany, Spain, and Sweden), in this article we create a rigorously constructed map of a transnational intelligence space. In order to make sense of interagency cooperation and modes of exchanging data, we analyze the structure of this space through the similarities and intractable differences that arise between the positions and discourses of these actors regarding their practices and the meaning they give to them.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.030 | 0.019 |
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".