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Record W4413953126 · doi:10.1093/jiplp/jpaf054

Understanding technology regulation through history: insights from the legal history of the printing press and copyright in early modern England

2025· article· en· W4413953126 on OpenAlexaff
Ali Ekber ÇINAR

Bibliographic record

VenueJournal of Intellectual Property Law & Practice · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsMcGill University
Fundersnot available
KeywordsPrinting pressLegal historyHistoryCopyright lawHistory of technologyLawPolitical scienceIntellectual propertyArchaeology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.072
GPT teacher head0.275
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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