Bibliographic record
Abstract
Purpose Canada's Chemical, Biological, Radiological, and Nuclear Research and Technology Initiative (CRTI) uses an operating model that is unusual in government. It is created to enable cross‐boundary capability and capacity building and learning. Some consider it a model for other federal science initiatives. The purpose of this paper is to explore the nature of leadership – and its relationship to perceived effectiveness – in this complex network of counter‐terrorism communities, where parts of the network are functioning better than others. At a more academic level, it explores whether complexity theory can inform leadership theory. Design/methodology/approach This qualitative, empirical study uses phenomenography and elements of ethnography as methodologies. Data are gathered through interviews and observation. Findings CRTI personnel refer to their initiative as a counter‐terrorism network of communities. The leader of each community works – without positional authority – with participants from many organizations and locations. The paper reveals qualitatively different ways of understanding leadership. Even though CRTI groups have much in common, participants' ways of understanding that work vary greatly. Some understand their work environments as complex systems rather than as traditional government structures; this way of understanding is associated with perceptions of effectiveness. This finding can change the ways in which science and technology professionals make sense of their work in complex, trans‐disciplinary fields such as counter‐terrorism and global warming. Originality/value This qualitative, empirical research complements and supports some of the conceptual work about leadership and learning in complex environments.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.020 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".