Convergence and Divergence in Tagging Systems: An Examination of Tagging Practices Over a Four Year Period
Bibliographic record
Abstract
This paper analyses the tagging patterns on delicious.com over a 4 year period using informetrics methods to assess how collaborative tagging supports and enhances traditional document indexing. Patterns in tag usage also highlighted practices related to personal and collective information organization which conventional systems are unable to facilitate.Cette communication analyse les modèles d'étiquetage sur delicious.com sur une période de quatre ans au moyen de méthodes informétriques dans le but d'évaluer comment l'étiquetage collaboratif appuie et augmente l'indexation traditionnelle. Les modèles d'utilisation des étiquettes mettent également en évidence des pratiques d'organisation individuelle et collective que les systèmes conventionnels ne peuvent soutenir.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.037 |
| Open science | 0.002 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".