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Record W4391813589 · doi:10.1515/mopp-2023-0062

Dimensions of Global Justice in Taxing Multinationals

2024· article· en· W4391813589 on OpenAlexaff
Peter Dietsch, Thomas Rixen

Bibliographic record

VenueMoral Philosophy and Politics · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsUniversity of Victoria
FundersAlexander von Humboldt-Stiftung
KeywordsTax evasionMultinational corporationEconomic JusticePublic economicsTax avoidanceCorporate governanceEconomicsPolitical scienceGlobal governanceDouble taxationLaw and economicsBusinessLawMicroeconomicsFinance

Abstract

fetched live from OpenAlex

Abstract Widespread tax evasion and avoidance have recently led to both significant reforms of international tax governance and increased attention from theorists of global tax justice. Against the background of an analysis of the double challenge of effectiveness and distribution facing the taxation of multinational enterprises, this paper puts forward a taxonomy of recent contributions of the tax justice literature. This taxonomy not only opens up an original angle of interpretation on global tax justice, but also provides a vantage point from which to evaluate recent reforms by the Organisation for Economic Co-operation and Development.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.019
Scholarly communication0.0080.004
Open science0.0000.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.044
GPT teacher head0.273
Teacher spread0.229 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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