Structured versus unstructured tagging: a case study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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 it