Propuestas para el análisis de colecciones de arte a través de metodologías y herramientas computacionales
Bibliographic record
Abstract
The methodology presented in this article proposes the use of tools that facilitate the identification, description, classification, visualization and manipulation of large quantities of information as well as the development of new forms of analysis, which offers the possibility of obtaining objective results from concrete consultations. In contrast with the traditional methods of investigation, the use of computational tools implemented in projects regarding digital humanities accomplishes different types of analyses from a common language and a basis of elaborated data which functions for a specific ontology and the necessities of each investigation. The display models, intuitive and direct, facilitate the identification and comprehension of the relations obtained from the data and characteristics of the works. Their elements, descriptors or authors, among other variables, make it possible to respond to very diverse interests. This versatility is precisely what makes this method very appealing for solving complex problem, and at the same time encourages the participation of multidisciplinary teams that can introduce new perspectives to common problems.
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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".