Algorithmic Regulation in Media and Cultural Policy: A Framework to Evaluate Barriers to Accountability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The word “algorithm” is best understood as a generic term for automated decision-making. Algorithms can be coded by humans or they can become self-taught through machine learning. Cultural goods and news increasingly pass through information intermediaries known as platforms that rely on algorithms to filter, rank, sort, classify, and promote information. Algorithmic content recommendation acts as an important and increasingly contentious gatekeeper. Numerous controversies around the nature of content being recommended—from disturbing children's videos to conspiracies and political misinformation—have undermined confidence in the neutrality of these systems. Amid a generational challenge for media policy, algorithmic accountability has emerged as one area of regulatory innovation. Algorithmic accountability seeks to explain automated decision-making, ultimately locating responsibility and improving the overall system. This article focuses on the technical, systemic issues related to algorithmic accountability, highlighting that deployment matters as much as development when explaining algorithmic outcomes. After outlining the challenges faced by those seeking to enact algorithmic accountability, we conclude by comparing some emerging approaches to addressing cultural discoverability by different international policymakers.
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.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it