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Record W2162049359 · doi:10.1177/0002716206298483

Why Not Share Rather Than Own?

2007· article· en· W2162049359 on OpenAlexaff
Russell W. Belk

Bibliographic record

VenueThe Annals of the American Academy of Political and Social Science · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsYork University
Fundersnot available
KeywordsSharing economyIncentiveBusinessCommerceIntellectual propertyBattleThe InternetDistribution (mathematics)CommodityOutsourcingInformation goodIndustrial organizationInternet privacyLaw and economicsMarketingEconomicsMicroeconomicsComputer scienceLawPolitical scienceWorld Wide WebFinance

Abstract

fetched live from OpenAlex

Sharing is an alternative form of distribution to commodity exchange and gift giving. Compared to these alternative modes, sharing can foster community, save resources, and create certain synergies. Yet outside of our immediate families, we do little sharing. Even within the family, there is increased privatization. This article addresses impediments to sharing as well as incentives that may encourage more sharing of both tangible and intangible goods. Two recent developments, the Internet and intellectual property rights doctrines, are locked in a battle that will do much to determine the future of sharing. Businesses may lead the way with virtual corporations outsourcing the bulk of their operations. Whether virtual consumers sharing some of their major possessions are a viable counterpart remains an open question.

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)0.009
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 score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.013
Scholarly communication0.0080.024
Open science0.0010.003
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0150.004

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 head0.095
GPT teacher head0.348
Teacher spread0.253 · 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

Citations808
Published2007
Admission routes1
Has abstractyes

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