Information Management in the Canadian Federal Government: Principles, Practices and the Role of Information Professionals
Bibliographic record
Abstract
Information management (IM) in the Canadian public sector is a complex area involving many professions such as librarians, archivists, records managers and information technology professionals. This exploratory study looks at the literature and experiential (qualitative) evidence from IM professionals in order to paint a picture of information management principles and practice in the Canadian federal government. Personal interviews were conducted with 20 librarians, information managers, records managers and other information professionals. Responses indicated that although the public sector has made tremendous strides in IM, there is often a gap between IM policy and practice as shown by inconsistencies and confusion in day to day operations compounded by the decimation of federal libraries (which are repositories of external as well as government information). The study also looks at roles of librarians and other IM professionals now and in the future. These professionals are well positioned to help close the gap between information policy and practice, moving forward toward more coordinated and integrated practices in information management as well as making information accessible and usable for their clients. Such functions aid the Canadian public sector in becoming a more effective knowledge organization.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.043 | 0.025 |
| Scholarly communication | 0.027 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".