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Record W7029020109

The impact of charity and tax law/regulation on not-for-profit news organizations

2016· report· en· W7029020109 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Minnesota Digital Conservancy (University of Minnesota) · 2016
Typereport
Languageen
FieldArts and Humanities
TopicHistorical Studies on Reproduction, Gender, Health, and Societal Changes
Canadian institutionsnot available
Fundersnot available
KeywordsDigital economyNews mediaWork (physics)Digital mediaKey (lock)
DOInot available

Abstract

fetched live from OpenAlex

Since the advent of the Internet, numerous digital news organizations have had difficulties remaining operational as commercial entities and the number of not- for-profit1 startups has grown. An important challenge for these news organizations is whether the legal systems in which they operate provide a conducive environment for charitable media and whether it can help explain their development. The legal qualification of news organizations as charities and the conferral of tax-exempt status are necessary to gather the necessary public support for their activities. However, in a number of jurisdictions, non-profit media outlets are often confronted with long-established legal frameworks that do not include journalistic activities within the concept of ‘charitable status’. These news organizations thus face significant delays and uncertainties during the process of obtaining tax-exempt status. This report contributes to the evolving debate on not-for-profit news start-ups by examining legal systems that determine whether charitable and tax exempt status and a variety of benefits associated with them can be granted. This report compares and contrasts policies, and assesses how such policies affect both the development of startups and existing news organizations that would like to become charities and gain tax-exempt status. It also provides an overview of best regulation practices in an attempt to tackle legal and societal challenges that need to be addressed. The study draws on the regulatory systems in five countries: Australia, Canada, Ireland, the United Kingdom (England and Wales), and the United States. We selected each of these countries on the basis that they share Anglo-influenced legal traditions. They are nevertheless subject to a mix of federal, state/provincial/territorial, and local regulation. The goal of this report is to gain a better understanding of the legal settings for charitable and tax exempt status for news organizations and challenges that may hinder their development. Drawing from the national cases, this report finds that not-for-profit media with charitable status exist more in the UK and the US than in Australia. Not-for- profit media entities have not significantly appeared in Canada and Ireland, with the exception of a few media outlets associated with other organizations that have charitable status. The primary hindrances to achieving charitable status for media organizations have been definitional, procedural, political, and commercial. The most significant hindrance has been the legal definitions of charitable purposes and the abilities of media organizations to meet those definitions. Some charitable/tax exempt news organizations exist in various forms in each country including charity-owned and controlled journalism in which charitable organizations own or control non-charitable journalism-producing organization. The national case studies also show the presence of some hybrid legal structures allowing journalistic-related organizations to receive gifts/grants (e.g. foundations, government aid, and educational structures). This report summarizes the issues raised by the respective countries to evaluate the potential for not-for-profit news. It also explores how it may be possible for startups and existing news organizations to become charities and gain tax- exempt status.

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)0.009
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.069
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0100.011
Scholarly communication0.0140.005
Open science0.0010.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0120.001

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 head0.059
GPT teacher head0.241
Teacher spread0.183 · 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 designObservational
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

Citations23
Published2016
Admission routes1
Has abstractyes

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