Knowledge Management and Social Media: A Case Study of Two Public Libraries in Canada
Bibliographic record
Abstract
It is important for all types of organisations including non-profit organisations (NPOs) to manage knowledge for effective and efficient utilisation of resources. Technology is considered as one of the key enablers of knowledge management (KM) practices but it can be costly to develop and implement in an organisation. With the advent of social media, NPOs such as public libraries have the opportunity to harness the power of technology for KM purposes as it is considered a low cost medium. A study was conducted, using an exploratory qualitative interview technique, in two contrasting public libraries: one is a large urban public library, and the other is a small rural public library. The data were analysed using a grounded theory approach informed by a social constructionist theoretical framework. This paper presents comparative findings from these case examples on their understanding of KM as a concept and their use of social media in management of knowledge. Results show that social media are valuable KM tools in public libraries, not only when directed externally for the purpose of promotion, but also to foster engagement with the public and collaborative work within the organisation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.040 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".