The Digital Memory Hole: Distortion and Accountability in the Age of New Media
Bibliographic record
Abstract
ABSTRACT Physical forms of media are increasingly being phased out and replaced by digital media. While this transition entails significant gains for consumers, it also presents risks that are worthy of attention and analysis. Thanks to the rise of channels of distribution such as streaming platforms, it is increasingly the case that content that has previously been made available in the marketplace can be disappeared or covertly edited in the absence of any significant scrutiny, criticism, or feedback. This paper presents two arguments regarding why this pattern of behavior may prove pernicious. The first argument posits that removing and revising culturally significant works is likely to distort people's understanding of the past, which has the potential to generate patterns of thinking and behavior that are corrosive for society. The second argument makes the case that the practice of removing and revising published content at will is damaging to the project of achieving accountability in the media marketplace. In light of these arguments, it is noted that there are alternative strategies for grappling with the existence of controversial pieces of media that are additive rather than subtractive, and do not pose a risk of generating distortion or undermining accountability.
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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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.059 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".