The United Kingdom: The impact of charity and tax law/regulation on not-for-profit news organizations
Bibliographic record
Abstract
To access this document please visit: http://bit.ly/29tg4s8. This is the UK chapter from a report published jointly by the Reuters Institute for the Study of Journalism, University of Oxford, and the Information Society Project, Yale University, edited by Picard, R. Belair-Gagnon, V. and Ranchordás, S. About the report: The advent of digital media means that many news organisations are re-thinking their business models, and facing new challenges. But one sector which has seen growth, is the not-for-profit start up industry. In a new report, published jointly by the Reuters Institute and the Information Society Project at Yale University, Robert H Picard, the RISJ’s North America Representative and colleagues examine the legal framework in which these operate in. Picard, along with Valerie Belair-Gagnon and Sofia Ranchordás (both Yale University), studies the challenges thrown up by legal systems which don’t include journalistic activities within the concept of ‘charitable status’. “Legal and regulatory definitions of charitable purposes hinder news organisations from achieving charitable and tax exempt status and receiving the associated benefits in Australia, Canada, Ireland, the United Kingdom, and the United States,” says Picard. Drawing on the regulatory systems of Australia, Canada, Ireland, The UK and The US, the report sets out to gain a clearer understanding of the legal frameworks for charitable and tax exempt status for news organisations and the distinct challenges that may hinder their development. This is the UK chapter of a co-edited report. Citation: Townend, J. 2016. ‘The United Kingdom: The impact of charity and tax law/regulation on not-for-profit news organizations’. In The impact of charity and tax law/regulation on not-for-profit news organizations, edited by Picard, R. Belair-Gagnon, V. and Ranchordás, S. Oxford/Yale: Reuters Institute for the Study of Journalism / Information Society Project, Yale Law School.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".