TIIARA: the “making of” a bilingual taxonomy for retrieval of digital images
Bibliographic record
Abstract
Purpose This paper aims to present the results of the second phase of a research project aiming to develop a bilingual taxonomy for the description of digital images. The objective of this second stage entailed the formal structuring of the taxonomy. It involved the choices of top‐level categories and their subcategories. Design/methodology/approach The taxonomy development process consists of several steps that are iterative in nature, and, as such, an incremental user testing needed to be carried out in order to validate and refine the taxonomy components. For the first validation phase, the card sorting technique was used. To increase the value of the testing, two different sorting exercises were performed by ten respondents, who completed feedback forms to provide comments and suggestions. Findings The analysis of the data provided by the card sorting exercises and the feedback forms highlighted the difficulties participants encountered using the taxonomic structure. This step was especially useful in understanding why the cards of a group were classified together. A summary of the decisions that were made following the first part of the validation process, as well as suggestions to improve the final version of the taxonomy, are also included. Originality/value The participation of the end‐users is of crucial importance in the taxonomy development. The card sorting method is generally used in domains such as psychology, cognitive science and web usability. For this project, it proved to be an invaluable source to identify difficulties encountered using the taxonomy structure and dynamically suggested ways to improve it.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".