Bibliographic record
Abstract
Calligraphic animation shifts the locus of documentation from representation to performance, from index to moving trace. Animation is an ideal playing field for the transformative and performative qualities that Arabic writing, especially in the context of Islamic art, has explored for centuries. In Islamic traditions, writing sometimes appears as a document or a manifestation of the invisible. Philosophical and theological implications of text and writing in various Islamic traditions, including mystic sciences of letters, the concept of latency associated with Shi‘a thought, and the performative or talismanic quality of writing, come to inform contemporary artworks. A historical detour shows that Arabic animation arose not directly from Islamic art but from Western-style art education and the privileging of text in Western modern art – which itself was inspired by Islamic art. A number of artists from the Muslim and Arab world, such as Mounir Fatmi (Morocco/France), Kutlug Ataman (Turkey), and Paula Abood (Australia) bring writing across the boundary from religious to secular conceptions of the invisible. Moreover, the rich Arabic and Islamic tradition of text-based art is relevant for all who practice and study text-based animation.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".