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Possessions and Self

2010· other· en· W1606543197 on OpenAlexaff
Russell W. Belk

Bibliographic record

VenueWiley International Encyclopedia of Marketing · 2010
Typeother
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsYork University
Fundersnot available
KeywordsPossession (linguistics)ImmortalityAestheticsMaterialismPsychology of selfIdentity (music)SelfCoproductionSocial psychologyPsychologySociologyArtEpistemologyLiteratureSocial sciencePhilosophy

Abstract

fetched live from OpenAlex

Abstract Possessions are central to our sense of self. For better or for worse, our possessions help to tangibilize our past, our present, and perhaps our future. They may even provide a type of immortality when they remain attached to our identity after our death. Shallow materialism has been found to be harmful, but there is another sense in which our treasured possessions provide a focus of attachment in an age of otherwise disposable consumption. Possessions also provide a sense of stability and continuity in our lives. And our relationship with possessions is seldom just a person–thing relationship, but rather a person–thing–person relationship as they connect us to, remind us of, and announce something about us to various other people in our lives. This article also addresses various special meanings of possession, including coproduction, brand tribes and brand images, counterfeits, cool objects, shared possessions, and possessions as gifts.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.010
Scholarly communication0.0040.004
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.239
Teacher spread0.230 · 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
GenreOther

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

Citations98
Published2010
Admission routes1
Has abstractyes

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