Intelligence Professionals' Views on Analytic Standards and Organizational Compliance
Bibliographic record
Abstract
In the present research, we aimed to examine the extent to which Canadian IC experts agreed with the directives for promoting analytic rigor captured in ICD 203. Although some Canadian intelligence professionals would be aware of ICD 203, Canada’s IC is not mandated to follow ICD 203, and Canada has no national equivalent to ICD 203. Thus, it would be instructive to see how IC experts view the ICD 203 elements in cases where there is no institutional pressure to agree. Moreover, we explored the factor structure of the 13 items that were used to tap attitudinal support for the ICD 203 facets. Doing so might prove useful for conceptualizing the main components of analytic rigor as currently captured in ICD 203. We also examined the extent to which these experts judged their organizations as being in compliance with the ICD 203 directives. Because the items we used to test personal agreement and organizational compliance were matched sets, we were also able to gauge where experts perceived the largest discrepancies between their professional values and their organizations’ behavior.
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 | 0.040 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".