Bibliographic record
Abstract
This paper introduces a new model of knowledge management enablers, which was designed to help Canadian Government leaders conquer the knowledge challenges of today. The review begins with an overview of the knowledge challenges facing the Canadian Public Service; clearly there are parallels in other public and private sector organizations. Next, a review of a number of knowledge management models concludes that five enablers are the most important. These five enablers are molded together to form a new exemplar based on the Japanese structure of the Torii. The results of a quantitative research project validate the Torii’s components of Technology, Leadership, Culture, Process and Measurement. However, as interesting as the model appeared to some groups, it simply did not garner support from the target audience. Ironically, it was neither the components nor the logic of the model, but rather it was the symbol itself that created challenges. Upon portraying the model as a symbol with which the desired end users could associate, there was immediate and unconditional acceptance. The lesson is clear, if one wishes to capture the imagination of potential users, the packaging may be as important the content. This paper is the culmination of a research project that began several years ago. Through formal presentations to various groups including: the World Congress on Intellectual Capital and Innovation; the Canadian Interdepartmental Knowledge Management Forum; the International Association of the Management of Technology and the National Securities Studies Course at the Canadian Forces College, the ideas of the project have been refined. The model presented in this paper is now deemed mature enough to share with the wider KM community. In the spirit of sharing and learning, it is hoped this paper will invoke debate and discussion so collectively we may become wiser.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".