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Record W6930769038 · doi:10.5281/zenodo.2596528

Sustainable Design Issues in Sincerity Expression: With the Case of Gift-Wrapping

2019· article· en· W6930769038 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytoplasmas and Hemiptera pathogens
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsSinceritySustainabilitySurpriseFocus groupGenerative grammarFocus (optics)Value (mathematics)

Abstract

fetched live from OpenAlex

People often sacrifice themselves for sustainability, but they do not often consider sustainability when serving others to express sincerity. In this study we focus on the activity of gift-wrapping<sup>1</sup>, as an important social behavior that creates notable waste. The purpose of this research is to investigate the general approach to gift-giving and gift-wrapping regarding sustainability, and how it might be possible to embody sustainability without degrading sincerity. A focus group and generative workshop were conducted respectively, and insights from the focus group were used when setting up the generative workshop. Through this study, we found that emotional value cannot be compromised by sustainability when expressing sincerity in gift- wrapping. Gift givers do not want to forfeit any sincerity by using eco-friendly materials that could be seen as inappropriate to the recipient. However, from the generative workshop, we found that people can show their true intentions by incorporating messages, by using conservable products that are reusable in other situations, by adding visual aesthetics, and by adding surprise aspects. This result shows that we can creatively adapt sustainability while keeping our emotional values. <sup>1 </sup>In this study, we differentiate the term 'gift-wrapping' from 'packaging'. It focuses on the additional decoration which can be even wrapped over the packages.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.997

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.029
GPT teacher head0.222
Teacher spread0.193 · 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.

Study designNot applicable
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

Citations0
Published2019
Admission routes1
Has abstractyes

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