CAPABILITY CHALLENGES IN THE HUMAN DOMAIN FOR INTELLIGENCE ANALYSIS: REPORT ON COMMUNITY-WIDE DISCUSSIONS WITH CANADIAN INTELLIGENCE PROFESSIONALS
Bibliographic record
Abstract
Abstract : Building on an earlier small-sample interview study (Derbentseva, McLellan, & Mandel, 2011), this report describes the findings of a focus group study with members of the Canadian intelligence community. The present study had both a larger and more diverse sample of intelligence practitioners than the earlier study. Four focus group discussions were conducted to explore human capability challenges within the broader Canadian community. The study also explored how behavioral science research might help the intelligence community deal with the identified challenges. A wide range of issues and challenges were identified by participants, including coordination and information sharing within the community, professionalization, the need for better career paths, improving recruitment practices, educating consumers about the intelligence process and products, clarifying the relationship between consumers and producers of analytic products and research, promoting collegial collaboration through informal networking, and understanding the role of mentoring and its impact on intelligence personnel. Overall, the study documents an expanded set of issues and challenges facing intelligence personnel, strong evidence of the potential contribution that future research can make toward alleviating current challenges within the intelligence community, and a detailed list of potential research opportunities for behavioral science researchers who wish to support intelligence capability.
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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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.050 | 0.013 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".