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Record W2060930006 · doi:10.5539/jpl.v2n4p44

Cooperatives’ Tax Regimes, Political Orientation of Governments and Rent Seeking

2009· article· en· W2060930006 on OpenAlexvenueno aff
Francesco Forte

Bibliographic record

VenueJournal of Politics and Law · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsLeaguePoliticsMarket economyEconomic policyBusinessConsumption (sociology)EconomicsPolitical scienceSociology

Abstract

fetched live from OpenAlex

The paper focuses on the strict relation between the continuous changes of the diversified tax regimes of the various types of cooperatives and the political composition of the governments succeeding each other in Italy from the after war period to the present. It emerged that the different types of cooperatives prevailing in the leftwing League of Cooperatives or in the Confederation of Italian Cooperatives (CCI) of catholic orientation or in both organization had a different possibility of exerting successful rent seeking with the various Governments with different political orientation. Consumption cooperatives operating in the mass retail which obtained generous tax treatments by Governments leaning to the left, lost most of them by centre right Governments. The cooperatives of agriculture and food processing, those of small fishing and those of production and labor, important both in the League and in CCI, were able to obtain from different Governments special privileges in the postwar period and kept most of them also under the centre right Governments of the last period. Social cooperatives object of a special legislation in 1992 under a Government of Giulio Andreotti, a leader close to the catholic organizations, kept their preferential tax regime with all the subsequent governments. They are important both in CCI and in the League and have a peculiar electoral power because of their capillary activities.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.246
Teacher spread0.232 · 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.

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

Citations5
Published2009
Admission routes1
Has abstractyes

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