Bibliographic record
Abstract
This article examines cryptocurrency trading in Turkey, focusing on the ‘gamblification’ of this emerging market. Based on 18 months of ethnographic research (2021-2022) conducted during an economic crisis exacerbated by the COVID-19 pandemic, the research reveals how Turks engaged with cryptocurrencies are considering the structural parallels between trading and gambling. The article also incorporates the perspective of Turkey's Directorate for Religious Affairs (Diyanet), which has declared cryptocurrency trading impermissible, highlighting the tension between contemporary financial practices and traditional Islamic frameworks. The article links the perception of cryptocurrency trading as a modern game of chance, as articulated by research participants, to Turkey's economic instability and their technological shift from traditional state-regulated games of chance (lotteries, betting on sports, and horse racing) to cryptocurrency trading. My ethnographic method brings new empirical data and qualitative analysis to understand the cultural and religious dynamics shaping this emergent financial phenomenon in the under-studied context of Turkey. I argue that cryptocurrency adoption in Turkey is driven by more than economic necessity; it reflects a cultural transformation valuing modernity and innovation. Many Turks view cryptocurrency as a viable alternative to traditional financial systems and a representation of the future of money. This shift signifies a departure from conventional monetary practices and reflects a collective idealisation of the future of finance. The article thus illuminates how Turkish individuals navigate risk and speculation during economic crises, demonstrating their adaptability in engaging with non-monetary financial markets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".