The Public Service in a Knowledge Based Society Innovation Research Team:
Bibliographic record
Abstract
has moved to define Canada as a 'knowledge based society', characterized by what is loosely labelled as the 'knowledge based economy ' (KBE). In its 1999 Throne Speech, the Canadian government challenged citizens and the public service to embrace necessary changes in ways of working together and in understandings of the importance of information as an economic asset: Knowledge and creativity are now the driving force in a new economy- our human talent, values and our commitment to working together will secure Canada's leadership in the knowledgebased economy (Throne Speech: 1999). Over the last several years the Canadian public service has responded to this emerging orientation through a number of proposals and strategic initiatives. Adopting the language of a select group of management consultants (Wenger and Snyder 2000; Wah 1999) and following steps taken by private sector firms to integrate IT into their business processes, the public service has undertaken a set of initiatives around 'knowledge management ' (KM- cf. Davenport and Prusak 2000). These initiatives have included the facilitation of information exchanges and problem solving by adapting email software, providing information to the public, to clients and co-workers via World Wide Web directories and information pages, and heightening awareness of the need to share information
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".