Library 2.0: balancing the risks and benefits to maximise the dividends
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide a number of examples of how Web 2.0 technologies and approaches (Library 2.0) are being used within the library sector. The paper acknowledges that there are a variety of risks associated with such approaches. The paper describes the different types of risks and outlines a risk assessment and risk management approach which is being developed to minimise the dangers while allowing the benefits of Library 2.0 to be realised. Design/methodology/approach The paper outlines various risks and barriers which have been identified at a series of workshops run by UKOLN (a national centre of expertise in digital information management based in the UK) for the cultural heritage sector. A risk assessment and risk management approach, which was initially developed to support use of Web 2.0 technologies at events organised by UKOLN, is described and its potential for use within the wider library community, in conjunction with related approaches for addressing areas such as accessibility and protection of young people, is described. Findings Use of Library 2.0 approaches is becoming embedded across many libraries which seek to exploit the benefits which such technologies can provide. The need to ensure that the associated risks are identified and appropriate mechanisms implemented to minimise such risks is beginning to be appreciated. Practical implications The areas described here should be of relevance to many library organisations which are making use of Library 2.0 services. Originality/value The paper should prove valuable to policy makers and web practitioners within libraries who may be aware of the potential benefits of Library 2.0 but have not considered the associated risks.
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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 | 0.026 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.037 | 0.027 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".