Chapter two: Waking the Poisoned Princess
Bibliographic record
Abstract
Chapter two: Waking the Poisoned PrincessWhen Canadian journalist and Istanbul resident Nick Ashdown had his mobile phone stolen and his mobile phone locator put the device somewhere in Tarlabaşı, Ashdown took to Twitter to rally the help of fellow Tweeps."Anyone in tight with this neighbourhood of Tarlabaşı?It's likely where my stolen phone is," he wrote, both in English and Turkish.The many replies to his seemingly innocuous question ranged from concern to open mockery."You still have your kidneys, right?Check them," one person tweeted, and another: "Even if it was an iPhone 20, nobody would dare to try."Others reverted to images to get the point across.A photograph of Sylvester Stallone as the movie character Rambo, holding a blazing machine gun, was captioned with: "There is only one man who would dare to go there."One Tweet, "Even John Wick can't get his phone from Tarlabaşı", in reference to a series of action movies featuring a retired killer-to-rent out for revenge, went viral.The thread itself became so popular that several Turkish news websites featured listicle pieces on Ashdown's Twitter request.It is unclear if the hapless journalist got his phone back.Why is this social media interaction important?It is unlikely that the Tweet would have gotten as much attention had the mobile phone locator turned up the device in another Istanbul neighbourhood.The Twitter exchanges and online comments show that people think they "know" how dangerous Tarlabaşı is, and the tweets assume this shared knowledge as a given.This is also why the joking comments on the journalist's request work: the question if anyone can help getting a stolen mobile phone back from Tarlabaşı is ridiculous only because the insiders to the joke "know" about the neighbourhood's terrible reputation, and those that do not are mocked as clueless.It is worth pausing to underline the degree to which the stigma attached to Tarlabaşı is pervasive knowledge, and as such, constitutes a social 'truth' so public that not only insiders or invested state actors were aware of it.One of the most puzzling experiences in that regard was with a family of Iraqi Christians from the city of Mosul I had befriended and who lived in the nearby neighbourhood of Kurtuluş.They had fled from Iraq to Turkey via the land route in 2010, after living conditions in their hometown had become untenable due to continuous sectarian violence and war.They told me about attacks on their church back at home in Iraq, about abductions and killings in the streets that had become commonplace.One evening I was sitting in their living room with the
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.015 |
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".