Bibliographic record
Abstract
In a rapidly changing global threat environment where risks transcend borders, private actors have developed their own intelligence capabilities and share information to mitigate risks from threats such as terrorism or civil unrest. While states support public-private cooperation to protect critical infrastructure, this is only part of the story. Existing research on intelligence cooperation does not adequately account for private sector actors. Furthermore, many studies explain cooperation through the existence of trust without exploring what drives that trust. This dissertation examines how public and private actors institutionalize intelligence cooperation through transnational horizontal networks. Venues for intelligence cooperation may facilitate any or all of: tactical information exchange and benchmarking, knowledge advancement, and strategic intelligence sharing. My project asks: what determines the degree to which private sector risk intelligence actors cooperate, and the extent to which they institutionalize this cooperation? My research relies on surveys, semi-structured interviews, and participant observation, and focuses on three cases: the U.S. State Department's Overseas Security Advisory Council, the U.S. Department of Homeland Security's Public-Private Analytic Exchange Program, and the private sector-led Analyst Roundtables. I also examine nascent cases of public-private intelligence-sharing institutions in Canada, including the Canadian Centre for Cyber Security, that focus on protecting critical infrastructure from cyber threats. Ultimately, I find that levels of intelligence cooperation are best explained through network ties-influenced by frequency of interactions-and by trust, which is driven by: size of group memberships; ability to identify mutual interests; and ability to vet each other for reliability. I also find that informal institutions substitute for formal institutions when the latter are not meeting actors' intelligence-sharing needs, and that the groups that are most conducive to deep levels of intelligence cooperation are not restricted by citizenship, but are instead transnational. The findings from this dissertation aim to (1) address the gap in intelligence cooperation literature with regard to non-state actors, (2) illuminate intelligence cooperation with private intelligence actors as a case of institutionalized cooperation that transcends borders, and (3) identify critical takeaways for public and private intelligence actors seeking to protect assets and lives.--Author's abstract
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
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 teacher head, 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".