Knowledge and Information Management Practices in Knowledge-Intensive Organizations: A Case Study of a Québec Public Organization
Bibliographic record
Abstract
This paper examines how a knowledge-intensive organization mobilizes and leverages its knowledge and information capabilities. The results indicate that in terms of information use, culture, and management, the respondents believe that they can use information effectively to solve work problems that their work benefits the organization, and that information sharing…Cet article présente comment une organisation à haute intensité de savoir mobilise et maximise ses capacités informationnelles et du savoir. Les résultats indiquent qu'en termes d'utilisation de l'information, de culture et de gestion, les répondants estiment pouvoir utiliser efficacement l'information pour réaliser leur travail, qu'il est utile à l'organisation et que le partage de l'information est essentiel pour le réaliser. L'information consignée et les mécanismes formels de transfert d'information et de connaissances sont aussi perçus comme les plus importants ...
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.030 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".