Patron Preferences for Folksonomy Tags: Research Findings When Both Hierarchial Subject Headings and Folksonomy Tags Are Used
Bibliographic record
Abstract
Objective: With the emergence of folksonomy as an option for subject tagging, discussions have ensued about the costs and benefits of continuing to construct and apply traditional subject headings, given that patrons now can generate their own tags. To date, there are very few databases that allow both systems to coexist. Methods: Within the full text ETD (Electronic Theses and Dissertations) database at Montana State University, both traditional, hierarchical subject headings and patron applied tags are allowed. Patrons are encouraged to tag and the database even features a browse tag capability and a featured ETD. After 24 months of coexistence, data was gathered and analyzed to determine patron use and preferences when given the option of adding their own tags. Results: Very few patrons take advantage of adding their own tags. After 2 years, only 2.5 percent of the ETDs have acquired folksonomy tags. A gradual replacement of LCSH headings by patron tags has not occurred. Conclusions: The main preference for patron-generated tags can be characterized as very narrow in application and in general would have been disallowed under a traditional library subject heading scheme. Despite the low usage, the folksonomy tags are a positive focal point and have generated collaboration within the database.
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.
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 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".