Introducing Web 2.0: wikis for health librarians
Bibliographic record
Abstract
This paper is an introduction to wikis for health librarians. While using wikis in health is now well established, their gradual rise is similar to other Web 2.0 tools such as blogs and RSS feeds. The same principles of collaboration, knowledge-sharing, and socialization apply to wikis. Easy-to-use, interactive, and built on open platforms (though not all are free), wikis offer a number of marketing and teaching opportunities for health librarians. Ironically, owing to the prominence of Wikipedia, which paved the way for the broader acceptance of Web 2.0 technologies, wikis are moving beyond the collaborative writing of encyclopedia entries. Wikis are now used for all kinds of projects, from managing internal library content to revising important reference sources such as the International Classification of Diseases (ICD). That said, some physicians and librarians express grave concerns about using wikis to create reference works—particularly, how questionable authority and editorial controls may result in medical errors. We argue that wikis were not necessarily meant to replace trusted print and digital information. When used responsibly as part of an overall content management plan, wikis can enhance our traditional collections and services. The authors predict that wikis will continue their rise in medicine through 2008, which will lead to other creative uses and applications in health libraries.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.019 |
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".