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Record W2573966271

Theoretical Perspectives on Ethical Dilemmas in Globalization and International Taxation

2016· article· en· W2573966271 on OpenAlexaboutno aff
George Joseph

Bibliographic record

VenueSSRN Electronic Journal · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsNormativeStakeholderLegitimacyViewpointsBusiness ethicsInternational taxationGlobalizationBase erosion and profit shiftingBusinessAccountabilityAgency (philosophy)Political scienceLaw and economicsPublic relationsPublic administrationEconomicsPublic economicsTax reformSociologyLaw
DOInot available

Abstract

fetched live from OpenAlex

Global corporations confront ethical dilemmas that lead to increasingly contentious environments where corporations extend tax planning by availing of ambiguities and new devices such as tax havens emerging from a globalized economic system. Adopting a normative theory-based approach, the paper contextualizes global tax planning approaches applying three theoretical perspectives, agency, legitimacy and normative stakeholder that help interpret ethical issues in global taxation to reflect the tensions and viewpoints of interest groups. The paper thereby extends the classifications of tax planning beyond those that emerge through the stages of historic growth of tax departments as profit centers and risk management centers (e.g., Donohoe, McGill, & Outslay, 2014) to stakeholder accountability centers. Specifically, drawing attention to the multiple stakeholders impacted by global business and international taxation, the paper presents a rationale for public policy that engages global business to further the public interest through a stakeholder based tax planning approach.

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)0.006
metaresearch head score (Gemma)0.005
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 score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0050.041
Scholarly communication0.0100.008
Open science0.0010.005
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.009
GPT teacher head0.238
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

Citations1
Published2016
Admission routes1
Has abstractyes

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