Structured versus unstructured tagging: a case study
Bibliographic record
Abstract
Purpose This paper seeks to describe and discuss a tagging experiment involving images related to Israeli and Jewish cultural heritage. The aim of this experiment was to compare freely assigned tags with values (free text) assigned to predefined metadata elements. Design/methodology/approach Two groups of participants were asked to provide tags for 12 images. The first group of participants was asked to assign descriptive tags to the images without guidance (unstructured tagging), while the second group was asked to provide free‐text values to predefined metadata elements (structured tagging). Findings The results show that on the one hand structured tagging provides guidance to the users, but on the other hand different interpretations of the meaning of the elements may worsen the tagging quality instead of improving it. In addition, unstructured tagging allows for a wider range of tags. Research limitations/implications The recommendation is to experiment with a system where the users provide both the tags and the context of these tags. Originality/value Unstructured tagging has become highly popular on the web, thus it is important to evaluate its merits and shortcomings compared to more conventional methods.
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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 | 0.022 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".