The holographic Atatürk: from commemorative pageantry to technological resurrection
Bibliographic record
Abstract
This article offers a fresh theoretical intervention in celebrity studies by exploring digital resurrection and necro-celebrity through the holographic projections of Mustafa Kemal Atatürk, often hailed as the founder of modern Turkey. A broad visual repertoire – ranging from municipal Republic Day shows and sports-club spectacles to AI-generated mash-ups – has fostered a ‘digital heritage economy’ that puts Atatürk’s iconic ‘Ebedî Şef’ (Eternal Chief) persona back into circulation in the twenty-first century. We weave together fame’s three classic strands – innate attribution, earned achievement, and media reproduction – within the twin frameworks of necro-branding and cultural necromancy, showing how a deceased figure can transcend both political capital and straightforward market value. Building on debates around simulacra and hyperreality, we treat celebrity holograms as local manifestations in speculative realism, with Atatürk’s case as our prism. Our contention is that the digital image does not supplant reality; instead of devouring the ‘real’, it surfaces latent possibilities under shifting representational regimes. By stressing that these projections are hyperstitional, not merely hyperreal, we uncover the feedback loop through which technological mediation lends tangible form to a long-standing myth lodged in collective memory.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".