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Record W4410526107 · doi:10.1163/9789004711525_018

Streaming, Copyright and Creators: a Canadian Perspective

2025· book-chapter· en· W4410526107 on OpenAlexaboutno aff
Graham Reynolds

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This paper will provide an introduction to streaming and copyright in Canada. It will proceed in five parts. Following this introduction, Part 2 of this paper will define streaming, discuss some of the benefits that flow from the widespread use of streaming services, and outline the extent to which streaming technologies are in use in Canada today. In so doing, this paper will distinguish between three types of streaming services: streaming services that secure authorization from rights-holders before making content available; streaming services that rely on technological measures or liability exemptions in order to host content in a non-infringing manner; and streaming services that host content in ways that prima facie infringe copyright. Part 3 of this paper will examine the legal status of streaming under Canada’s Copyright Act. Over the past decade, a number of amendments have been made to the Copyright Act in response to the emergence of streaming services. Similarly, a number of judicial decisions have considered whether and the extent to which streaming infringes copyright in Canada, and if so what remedies might be available to copyright owners. These amendments and decisions will be described in this section, along with a general discussion of copyright in Canada as it relates to streaming services. Part 4 will address the impacts of streaming on Canadian creators. It will begin by discussing the consequences, for creators’ audiences and incomes, of the ongoing shift from a traditional distribution model to a streaming model. Next, it will describe how a number of programs meant to assist Canadian creators – including those that require broadcasters to provide funding to assist Canadian creators and to present a certain percentage of Canadian content – do not extend to online streaming services. As part of this discussion, it will survey a number of recent attempts to reform Canada’s broadcasting framework as it relates to streaming services. Bill C-10, a recent (and heavily criticized) attempt to bring streaming services within the ambit of Canada’s broadcasting regulatory regime, died on the order paper on 15 August 2021, when Governor General Mary Simon dissolved Parliament at the request of Prime Minister Justin Trudeau, triggering an election campaign. On 2 February 2022, an updated version of this bill (now known as Bill C-11, the Online Streaming Act) was introduced by the federal government. This bill received royal assent on April 27, 2023. Building on the above analysis, this paper will conclude in Part 5 by highlighting three points that in the view of the author should be kept in mind in the context of any debate about streaming and copyright in Canada: the importance of ensuring universal access to streaming services; the importance of preserving the balance between copyright owners’ rights and the public interest; and the need to ensure that any discussion about legal reforms relating to streaming foregrounds the question of how best to support creators in light of the ongoing shift to streaming-based media.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.142
Threshold uncertainty score0.995

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.007
Science and technology studies0.0230.024
Scholarly communication0.0140.008
Open science0.0020.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0240.001

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.

Opus teacher head0.014
GPT teacher head0.200
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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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