Bibliographic record
Abstract
Abstract A relatively small number of global Internet giants—Google, Apple, Facebook, Amazon, Microsoft, and Netflix—have come under intense and ongoing fire for precipitating a twin crisis of journalism and the media, destroying democracy, and centralizing control over the Internet. In response, a new wave of Internet regulation is now in the making in one country after another. This chapter agrees that a forceful response to the platforms is overdue but raises concerns that the case against GAFAM + has become orthodoxy, anchored in cherry-picked evidence and a tendency to see these firms as the cause of all perceived woes. I also argue that while attempts to regulate digital platforms by the standards of broadcasting regulation may be politically expedient, this approach rests on superficial analogies. It also ignores the fact that the media industries have developed in close proximity to the vastly larger telecoms, consumer electronics and banking firms since the mid-nineteenth century. The last sections of this chapter offer four principles of structural and behavioural regulation drawn from this history as guides for a new generation of internet regulation today: structural separation (break-ups), line of business restrictions (firewalls), public obligations and public alternatives.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".