MétaCan
Menu
Back to cohort
Record W3080558564

Facebook’s Libra: The Next Tax Challenge for the Digital Economy

2020· article· en· W3080558564 on OpenAlexaff
Allison Christians, Mahwish Tazeem

Bibliographic record

VenueSSRN Electronic Journal · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsMcGill University
Fundersnot available
KeywordsScrutinyDigital economyScope (computer science)BusinessCryptocurrencyValue (mathematics)Plan (archaeology)EconomicsPolitical scienceComputer securityComputer science
DOInot available

Abstract

fetched live from OpenAlex

In 2019 Facebook announced its plan to launch a cryptocurrency within the coming year, with the express goal of extending past its existing user base to those who lack access to traditional banking services. The prospect of Facebook enabling millions of global transactions outside of the (highly regulated) conventional banking system prompted scrutiny, most immediately in the area of financial services regulation. But it should also heighten the sense of urgency to reconstruct prevailing global tax rules to ensure that highly digitalized businesses pay an appropriate amount of taxes wherever they carry out business activities and create value. This paper lays out Facebook Libra’s original design concept, the problems it sought to solve, and the potential implications its successful launch would have on the redesign of the global tax system that is already in progress. The paper concludes that the current global economic turmoil makes for an uncertain future, but one that will clearly require a coherent strategy for taxing high tech firms and innovations with global scope, with Libra a prominent case.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0010.000
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 head0.030
GPT teacher head0.212
Teacher spread0.182 · 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.

Study designTheoretical or conceptual
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueSSRN Electronic JournalSame topicFinTech, Crowdfunding, Digital FinanceFrench-language works237,207