The Dynamics of Collaborative Tagging: An Analysis of Tag Vocabulary Application in Knowledge Representation, Discovery and Retrieval
Bibliographic record
Abstract
This study investigates the contribution of collaborative tagging to the design of user-driven vocabularies in knowledge management systems (KMS). Three metrics, tag growth, tag reuse, and tag discrimination, were used to examine the evolution of the tagging vocabulary of the knowledge management community of interest in CiteULike over a three-year period. Results indicate a steady decrease in the number of unique tags over the four years, suggesting an increasing stability in the community vocabulary over time and the establishment of domain-specific vocabulary. Members reused each others' tags over time and exhibited increasingly collaborative tagging behaviour. Tag discrimination was high, with 4.11 distinct articles per tag. The stable and discriminatory nature of the community's tags suggests that collaborative tagging may serve as a useful resource for vocabulary choice or maintenance by KMS managers.
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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 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".