Introduction: the continuing evolution of social tagging
Bibliographic record
Abstract
The genesis of an idea: Louise's perspective I was introduced to the concept of social tagging when I was asked by Library and Archives Canada to speak about folksonomies at a metadata conference in Ottawa in 2005. Although I had heard the term, which was coined by Thomas Vander Wal (2007) in 2004, I did not know much about it, but I was certainly interested in the opportunity to learn more about this concept. As with most scholars in this field, my first in-depth exposure to the concept of social tagging was Adam Mathes’ now classic article on folksonomies (2004). My area of expertise was in the areas of cataloguing, classification and thesaurus con - struction, all areas where language and descriptors are carefully chosen and controlled by professional information managers. I became intrigued at the possibilities that social tagging could provide to our carefully curated metadata records in libraries, which was the basis for my first article on the topic on social tagging (Spiteri, 2006) and which opened a new area of research interest that has continued to grow over the years. For several years, I have studied the contributions of social tagging to library discovery systems (Spiteri, 2006; 2007; 2009); my interest in this particular topic was inspired by courses I teach in the areas of the organization of information, cataloguing and classification, as well as my involvement in social reading sites such as LibraryThing and Goodreads. I was struck by the dynamic and interactive nature of these reading sites: readers voluntarily edited metadata records for books, added social tags to describe content, created and shared reading lists, engaged in discussions with other readers, wrote reviews of items they had read and responded to reviews written by others. I was struck also by the difference between these dynamic sites and the static nature of the public library catalogues that I used, and used as exemplars for my students. These catalogues contained carefully constructed metadata records, using established and standardised metadata standards such as Anglo-American Cataloguing Rules and, more recently, Resource Description and Access, codified via the MARC (MAchine Readable Cataloging) framework and standard Library of Congress Subject Headings to describe the content and genre of a work.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.018 |
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".