Open-Source Sephardism: Heritage Re-Inscriptions of Haketia in Virtual Worldly Commons
Bibliographic record
Abstract
Haketia, a hybrid Judaeo-Spanish trans-language suppressed under imperial rule in the Maghreb, is being actively reanimated through digital heritagisation practices amongst dispersed communities of speech. How do digital heritage practices enable the postvernacular transformation of Haketia from suppressed vernacular to an active tool of cross-cultural coalition-building? Drawing on virtual ethnography of the eSefarad online platform, this study examines how such platforms operate not as static preservation but through processes of ‘trans situ’ heritagisation, where cultural elements are exchanged across multiple sites, temporalities, and modes of presence. The analysis traces Haketia’s transition to postvernacular performance, where using the language becomes a conscious cultural enactment that forges virtual communities across historical rupture. Rather than representing continuous transmission, these digital practices are marked by inventive reconstruction and purposeful reassembly, conceptualised here as ‘open-source Sephardism’ – a framework grounded in diasporism that privileges relational ‘hereness’ over territorial return. Through collaborative negotiation and cross-cultural coalition, this digital heritage practice fosters the revival of Judaeo-Muslim virtual worldly commons, demonstrating how minoritised vernaculars can be reactivated as living threads of diasporic connection that transcend traditional boundaries of heritage preservation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".