The Third-Shift Problem in china: The First Step is Admitting You Have a Problem
Bibliographic record
Abstract
Death by Apple iPhone seems almost impossible yet on July, 11, 2013, it became the harsh reality for a twenty-three-year-old flight attendant from China's Xinjiang region. 2 Ma Ailun, a flight attendant for China Southern Airlines, was electrocuted after answering a call on her iPhone while it was charging. 3 An investigation into Ma's death revealed that Ma was not using an official Apple iPhone charger, but rather a counterfeit third-party charger. 4 In response to Ma's death, Apple initiated a "USB Power Adapter Takeback Program" where customers could turn in third-party chargers at any Apple Retail Store or Apple Authorized Provider and purchase an official Apple charger for the discounted price of ten dollars. 5 While the program was initially aimed at American and Chinese Apple consumers, Apple eventually extended the program to a number of other countries, including Canada and the United Kingdom. 6Ma's untimely death and the events surrounding it were the headline for news outlets across the globe and would become an even bigger story when a similar incident was reported less than a week later.1 "Paper tiger,"( in simplified Chinese) is a Chinese metaphor that refers to a particular subject appearing strong, but in reality is weak or powerless.For more information on the history of the metaphor see Henry Yuhuai He, Dictionary of the Political Thought of the People's Republic of China 649 (2001). 2 Electrocution Death Blamed on Charging iPhone, CBCNews (Jul.15, 2013), http://www.cbc.ca/news/technology/electrocution-death-blamed-on-charging-iphone-1.1313114.3 For the purposes of this Article, Chinese names are written with the family name first. 4 Sean Levinson
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".