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The Impact of the Digital Economy on the Upgrading of Resident Consumption

2024· article· en· W4405332389 on OpenAlexaff
Yijie Weng

Bibliographic record

VenueAdvances in Economics Management and Political Sciences · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicImpulse Buying and Technology Impacts
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsConsumption (sociology)Digital economyPopularityQuality (philosophy)Consumer spendingBusinessDigital transformationEnvironmental economicsEconomyEconomicsComputer sciencePolitical scienceSociology

Abstract

fetched live from OpenAlex

With the continuous advancement of technology, the digital economy is gradually integrating into people's lives. For the public, innovations in economic models mean that the quality of daily life is subtly changing, with the most direct external manifestation being consumption. This paper uses a literature review approach, based on the research conclusions of multiple studies, to explore in depth the impact of the digital economy on residential consumption upgrading from two perspectives: residents' consumption levels and consumption environments. The study shows that the popularity of the digital economy has led to the development of new consumption concepts and created a more advanced way of consumption. The study also found most industrial structures' digital transformation aligns with the needs of the middle and low-income groups in society, which can greatly promote consumption among these groups and improve their quality of life. Additionally, the concepts and technologies of the digital economy have created a more comfortable and reliable ecological environment for consumers, significantly promoting the iterative upgrading of residential consumption.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
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 head0.028
GPT teacher head0.283
Teacher spread0.256 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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