Bibliographic record
Abstract
Purpose Using Weick's sensemaking theory within a KM framework, and storytelling methodology, this study aims to deconstruct a recent public internet access policy crisis at the newly amalgamated Ottawa Public Library (Canada). As the library's former Manager of Virtual Library Services, the author retrospectively enacts the story of how the library board and management resolved a public controversy led by the staff and the community newspaper. At issue were the library staff's right to be protected from viewing internet pornography, the community's reaction to the issue of protecting children's internet access, and the library's commitment to intellectual freedom online. Design/methodology/approach Plausible meanings are presented, the public library's identity and beliefs are reinterpreted, organizational vocabularies are challenged and tacit and cultural knowledge is created and shared. Findings In keeping with a commitment to knowledge creation and use, the library should be actively engaged in multiple tellings of this organizational story by both staff and management. Such tellings, while perhaps not building any new consensus, would contribute to future sensemaking and could aid future strategic planning. Originality/value Applies Weick's theory, developed in a larger KM framework, and using storytelling methodology, to deconstruct the experience of a recent organizational crisis involving public internet access in a Canadian public library.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.030 | 0.063 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".