Shares of TV providers drop as AT&T warns of video losses
Bibliographic record
Abstract
AT&T said it lost 90,000 video subscribers in the U.S. in the third quarter. Itâs a steeper drop than the same period last year, even though gains from its newer, cheaper online cable-like service, DirecTV Now, are included. DirecTV Now wasnât available in the July-September quarter in 2016. More and more people are cutting the cord when it comes to their television viewing...and that has providers and investors scrambling.AT&T said it lost 90,000 video subscribers in the U.S. in the third quarter.It's a steeper drop than the same period last year, even though gains from its newer, cheaper online cable-like service, DirecTV Now, are included.AT&T, which is also the No. 2 wireless carrier in the U.S., blames tough competition from both traditional TV providers like Comcast and newer digital-video services like YouTube TV.It also blames the impact from hurricanes and stricter credit standards for customers.AT&T's prediction echoes Comcast's forecast in early September of third-quarter losses of 100,000 to 150,000 video customers .That would be Comcast's largest quarterly loss since 2014. Comcast also blamed competition and weather.Rising prices for traditional TV bundles and those growing digital options are increasingly driving customers online and away from traditional TV.More losses are expected. UBS analyst John Hodulik said that cord cutting continues to gain steam as streaming TV builds momentum.""
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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; both teacher heads agree on what is shown here.
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".