“An Hundred Stories in Ten Days”: COVID-19 Lessons for Culture, Learning, and Copyright Law
Bibliographic record
Abstract
In the face of a pandemic, copyright law may seem a frivolous concern; but its importance lies in the ever-expanding role that it plays in either enabling or constraining the kinds of communicative activities that are critical to a flourishing life. In this article, we reflect on how the cultural and educative practices that have burgeoned under quarantine conditions shed new light on a longstanding problem: The need to recalibrate the copyright system to better serve its purposes in the face of changing social and technological circumstances. We begin by discussing how copyright restrictions have manifested in a variety of contexts driven by the coronavirus lockdown, focusing first on creative engagement and then on learning, foregrounding the damage done by encoding a permission-first approach into governance structures and digital platforms. These stories unsettle the common copyright narrative— the one that tells us that copyright encourages learning and the creation and dissemination of works—laying bare its disconnect from the current realities of our digital dependency. Turning to consider the justifications for copyright control, we underscore the critical role of user rights and substantive technological neutrality in crafting a flexible and fair copyright system for the future. The article concludes with some lessons that might be drawn from these tales of copyright in the time of COVID19 to inform the development of new digital copyright norms for whatever “new normal” emerges.
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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.086 |
| Scholarly communication | 0.027 | 0.039 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 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".