TIIARA: the “making of” a bilingual taxonomy for retrieval of digital images
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 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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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