Role of Information Professionals in Knowledge Management Programs : Empirical Evidence from Canada
Bibliographic record
Abstract
The objective of this study is to provide empirical evidence of the role of information professionals in knowledge management programs. 386 information professionals working in Canadian organizations were selected from the Special Libraries Association’s Who’s Who in Special Libraries 2001/2002 and questionnaire with a stamped self-addressed envelope for its return was sent to each one of them. 63 questionnaires were completed and returned, and 8 in-depth interviews conducted. About 59% of the information professionals surveyed are working in organizations that have knowledge management programs with about 86% of these professionals being involved in the programs. Factors such as gender, age, and educational background (i.e. highest educational qualifications and discipline) did not seem to have any relationship with involvement in knowledge management programs. Many of those involved in the programs are playing key roles, such as the design of the information architecture, development of taxonomy, or content management of the organization’s intranet. Others play lesser roles, such as providing information for the intranet, gathering competitive intelligence, or providing research services as requested by the knowledge management team.
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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".