Social tagging in the life sciences: characterizing a new metadata resource for bioinformatics
Bibliographic record
Abstract
BACKGROUND: Academic social tagging systems, such as Connotea and CiteULike, provide researchers with a means to organize personal collections of online references with keywords (tags) and to share these collections with others. One of the side-effects of the operation of these systems is the generation of large, publicly accessible metadata repositories describing the resources in the collections. In light of the well-known expansion of information in the life sciences and the need for metadata to enhance its value, these repositories present a potentially valuable new resource for application developers. Here we characterize the current contents of two scientifically relevant metadata repositories created through social tagging. This investigation helps to establish how such socially constructed metadata might be used as it stands currently and to suggest ways that new social tagging systems might be designed that would yield better aggregate products. RESULTS: We assessed the metadata that users of CiteULike and Connotea associated with citations in PubMed with the following metrics: coverage of the document space, density of metadata (tags) per document, rates of inter-annotator agreement, and rates of agreement with MeSH indexing. CiteULike and Connotea were very similar on all of the measurements. In comparison to PubMed, document coverage and per-document metadata density were much lower for the social tagging systems. Inter-annotator agreement within the social tagging systems and the agreement between the aggregated social tagging metadata and MeSH indexing was low though the latter could be increased through voting. CONCLUSION: The most promising uses of metadata from current academic social tagging repositories will be those that find ways to utilize the novel relationships between users, tags, and documents exposed through these systems. For more traditional kinds of indexing-based applications (such as keyword-based search) to benefit substantially from socially generated metadata in the life sciences, more documents need to be tagged and more tags are needed for each document. These issues may be addressed both by finding ways to attract more users to current systems and by creating new user interfaces that encourage more collectively useful individual tagging behaviour.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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; a candidate call from one teacher head, 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".