Knowledge Intensive Work in a Network of Counter-Terrorism Communities
Bibliographic record
Abstract
Knowledge management is often associated with the need for change and related shifts in ontologies, ways of knowing and ways of working. Combine the centuries-old debates about what defines knowledge with proposed paradigm shifts to become knowledge-oriented, focused on inter-relationships, and cognisant of the complex and voluntary nature of knowledge work, and there is bound to be controversy and ambiguity. However, knowledge management research and practice becomes more focused and less ambiguous when set in the context of an urgent need. This chapter describes a study of a Canadian public sector science initiative. The terrorist attacks of 9/11 catalyzed ripples of reflection and innovation over great distances. In Canada, the federal government initiated the Chemical, Biological, Radiological and Nuclear (CBRN) Research and Technology Initiative (CRTI) to enable learning and progress, using what is essentially a communities of practice model. CRTI established a knowledge management office, to help this network of communities generate, share and use tacit and explicit knowledge. Some aspects of the initiative were working better than others and I was asked to conduct research to explore how CRTI members understand their work in a complex, knowledge-rich environment. I collected data through interviews and observation, and used phenomenography: a qualitative methodology from Scandinavia, which reveals qualitatively different ways of understanding phenomena. Phenomenography is usually driven by the desire to improve something, rather than simply to deepen understanding. As part of the analysis, I used a model for understanding communities of practice that was developed by [then] Major Pete Kilner in his work with the internationally respected CompanyCommand community.Participants who understood their work as complex and unpredictable tended to emphasize connections and relationships, focused on learning more than doing, spontaneously referenced all aspects of Kilner’s model, saw knowledge as more of a flow than a thing, and were more satisfied with their individual and community effectiveness. This research had added value in that CRTI is considered successful and is being considered as a potential model for other science and technology work in the Canadian public service. The research has implications for knowledge-intensive work in complex environments and suggests that there is fertile ground for more qualitative research that integrates thinking from knowledge management and complexity thinking.
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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.028 | 0.034 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".