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Record W4399979727 · doi:10.1002/mar.22063

Presents with presence: The role of memory and essence in heirloom gift appreciation

2024· article· en· W4399979727 on OpenAlexaff
Ali Tezer, Matthew Philp, Ethan Pancer, Queenie Zhu

Bibliographic record

VenuePsychology and Marketing · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsSaint Mary's UniversityToronto Metropolitan UniversityHEC Montréal
Fundersnot available
KeywordsValue (mathematics)Context (archaeology)PsychologyGift givingSocial psychologyAestheticsAdvertisingArtHistoryPolitical scienceLawBusinessComputer science

Abstract

fetched live from OpenAlex

Abstract Choosing the right gift is a common challenge. Despite recent research suggesting that sentimental gifts are most appreciated, it remains unclear of the underlying value of such gifts. The current research examines sentimental gifts in the context of heirlooms and demonstrates that the value of such sentimental gifts comes from their memory value (being a reminder of loved ones) and essence value (being a receptacle of their spiritual presence). These findings emphasize the importance of prior physical contact with an item on the appreciation of heirloom gifts and demonstrates how ordinary store‐bought gifts can benefit from increased appreciation when framed as future heirlooms (being gifted with the intention to be 1 day gifted to future generations). Three experiments find support for these predictions. This research contributes to the understanding of gifting by exploring the mechanisms underlying heirloom gift appreciation and offers guidance to those looking to give gifts that will be well received.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.262
Teacher spread0.250 · 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 designObservational
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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