Bibliographic record
Abstract
INTRODUCTION AND OVERVIEW 1.Facebook, Inc. ("Facebook") sells advertising services to individuals and businesses desiring to promote goods or services on the Facebook platform.1 Facebook represents that it has 10 million business advertising customers, the vast majority of which are small and medium-sized businesses. 2 Plaintiffs are among the businesses that have purchased Facebook's advertising services.2. Modern business advertisers like Plaintiffs have little choice but to advertise with Facebook.Facebook (which now owns Instagram and WhatsApp) has over 70% market share in the social media market.3 If a business wants to avoid using Facebook advertising, it will reach only about 30% of the social media market.3. Facebook's own Advertising Policies, which are integrated into the Facebook Terms of Service, explain the ad review process and state that if an ad is disapproved, Facebook will provide an email with details explaining how the user can create a compliant ad. 4 To this day, those Advertising Policies state that "[i]f your ad doesn't get approved, we'll send you an email with details that explain why.Using the information in your disapproval email, you can edit your ad and create a compliant one."4.Contrary to this representation, and starting at least as early as the second quarter of 2019, Facebook has routinely rejected ads without providing an explanation sufficient to enable the advertisers to create compliant ads.Plaintiffs' advertisements are among the ads that have been rejected without the explanation promised under Facebook's policies.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.111 | 0.028 |
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".