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Record W3204146807

Policy Forum: Promoting Tax Compliance by Regulating the Digital Economy - Quebec's Uber Initiative

2021· article· en· W3204146807 on OpenAlexaffabout
Michaël Robert-Angers, Luc Godbout

Bibliographic record

VenueSSRN Electronic Journal · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsBusinessDigital economyTax revenueContext (archaeology)CommodityRevenueMultinational corporationValue-added taxTax reformGovernment (linguistics)Tax policySales taxAd valorem taxDouble taxationEconomic policyFinancePublic economicsEconomicsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

The development and expansion of the digital economy is changing how companies interact with their customers and suppliers. Digital business models facilitate transactions between individuals and make it easier to conduct business abroad without the need for a physical presence. However, the growing use of such models creates many challenges for tax administrations. In particular, these new business practices call into question the traditional ways of collecting tax revenues, and thus force tax administrations to innovate. In Quebec, the context surrounding the legalization of the operations of the multinational Uber has led to an agreement between the company and the provincial government providing that Uber will carry out, on behalf of the drivers using its platform, tax compliance activities that employers would normally perform. Specifically, Uber now pays the sales tax applicable to drivers' transactions directly to Revenu Quebec. This arrangement helps to protect commodity tax revenues in an economic sector where tax evasion is prevalent.

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)0.004
metaresearch head score (Gemma)0.006
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.376

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0050.002
Open science0.0030.002
Research integrity0.0100.004
Insufficient payload (model declined to judge)0.0350.002

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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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
GenreCommentary

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
Published2021
Admission routes2
Has abstractyes

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