Facebook and Google as Offices of Censorship
Bibliographic record
Abstract
Abstract On May 15, 2018, Facebook continued its springtime campaign to restore its reputation in the aftermath of the Cambridge Analytica scandal. A “Transparency” report was published, which included statistics on the extent of content removal, organized by category. Let’s look at for instance the category “Graphic Violence”: “In Q1 2018, we took action on a total of 3.4 million pieces of content, an increase from 1.2 million pieces of content in Q4 2017. This increase is mostly due to improvements in our detection technology, including using photo-matching to cover with warnings photos that matched ones we previously marked as disturbing. These actions were responsible for around 70% of the increase in Q1”. The numbers may seem high, but they only tell half the story. Another graph in the report shows that 71.56% of the 1.2 million users were tracked by Facebook itself, until user complaints started flooding in; in the first quarter of 2018, this figure rose to 85.6%. The fact that the number of removals tripled means that content removed because of user complaints rose from 341,000 to almost 500,000, in absolute figures, despite the decrease in percentage. So, the increase can be attributed not only to better tracking equipment, but also to more complaints favored. These numbers are a testimony to content removal on a disproportionately large scale, also known as censorship.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".