A Right to Reach an Audience: An Approach to Intermediary Bias on the Internet
Bibliographic record
Abstract
Some of the thorniest problems of communications law and policy were supposed to have been solved by the Internet. The issue of who can speak, or access the means of speech, was said to have been solved by the arrival of ubiquitous, relatively cheap access to the Internet. The problem of media concentration was supposed to have been solved now that so many more speakers could contribute. While the Internet has undoubtedly assisted with these problems, new gatekeepers have arisen, and that their actions are not necessarily supportive of a healthy, pluralistic communications environment. While the problem of access to the means of speech seems to have been greatly alleviated by the Internet, the chokepoint has now shifted downstream to a class of intermediaries that select and filter information en route to listeners. Examples of this class of selection intermediaries include search engines, software filters, Internet Service Providers (ISPs) that block or filter content, and spam blocklists. It is true that we have long been surrounded by too much information, and we have relied on various intermediaries to assist us in finding and choosing information. Why, then, is the role of selection intermediaries on the Internet worthy of comment? In my view, the Internet offers an opportunity for us to craft new approaches to the selection intermediary function in a way that enables us to keep as much of the speech freedom engendered by the Internet as possible. There is a danger that by reflexively drawing analogies to familiar old selection intermediaries, such as libraries, we will tolerate selection criteria that erode the freedom of speech made possible on the Internet. In the age of the Internet, a complete theory of communication rights must explicitly address the effects of selection intermediaries and recognize as protected each of the steps involved in the communicative relationship between speaker and listener. This includes not only the right to speak and the right to hear (which are already recognized forms of free speech rights), but also the right to reach an audience free from the influence of extraneous criteria of discrimination imposed by selection intermediaries. If selection intermediaries block or discriminate against a speaker on grounds that listeners would not have selected, that speaker's ability to speak freely has been undermined. The paper makes a case for the recognition of this right. It also considers whether government regulations to give effect to this right could be imposed without violating the free speech rights of selection intermediaries.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 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".