Museos de arte y TICs: usos, tipologías, ejemplos y derivaciones
Bibliographic record
Abstract
Art museums -and specially those that shelter modern or contemporary art - are\nusing the TICs in search of a connection, a "bridge" between the public and their collections.\nThe bad relation that takes place, in many occasions, between art and public forces the\npersons in charge of the museums - specially to the managers of making the departments of\neducation and diffusion - to looking for all kinds of solutions, often to innovating and, almost\nalways, acting of "forefront". If to this fact we add the incorporation of the TICs in the new\nforms of contemporary creation, we think that this need is intensified in the museums, rooms\nand, in occasions, galleries, dedicated to exhibiting the most recent work. This way, in the use\nof the TICs we find "ultramodern" offers that, from the museums of modern and\ncontemporary art, spread to the museums of art in general, to other kind of museum, to formal\neducation and to other spaces of non formal education.\nThere are a lot of ways of employing the TICs inside the didactics programs of these spaces,\nfrom those that include them as tool for the interpretation, up to those others that use them to\nform a "technological" parallel museum. This way, we propose in the present papper an\narrangement and analysis of the different forms of use of the TICs on art museums, thinking\nabout the museologies of education that remain latent after them, and proposing, in every\ntypology, some examples of Spanish, American, Canadian, English, French, Tunician and\nItalian museums. Finally, we propose a reflection on the repercussion and derivations that\nthese different ways of approximating to the TICs suppose for the Didactics of the Social\nSciences in formal education.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".