Understanding technology regulation through history: insights from the legal history of the printing press and copyright in early modern England
Bibliographic record
Abstract
Abstract Studying legal history is crucial for understanding both the past and present of technology regulation, as past societies confronted challenges similar to those we face today. However, the scarcity of such studies in the existing literature highlights the need for a deeper exploration of how technology has historically shaped the legal framework. This article addresses this gap by examining the legal history of the printing press in early modern England. I analyse the legal responses to the printing press from the late fifteenth to the early eighteenth century, drawing on both legal history and Braudel’s longue durée approach. In doing so, I identify four key characteristics of technology regulation during this period: initial support followed by increasing constraints, regulation driven by interactions among interest groups, the necessity of disruptive externalities for corrective action and the influence of economic power in shaping regulatory landscape. I argue that these characteristics offer two crucial insights for the history of technology regulation. First, influencing technology regulation required both economic power and a direct stake in the technology; second, the involvement of diverse interest groups was essential to mitigate potential complications.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".