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Record W3083627157

패션산업과 거시 변수들간의 관계-패션 상장기업 중심으로-

2020· article· ko· W3083627157 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venue한국의류산업학회지 · 2020
Typearticle
Languageko
FieldBusiness, Management and Accounting
TopicConsumer Perception and Purchasing Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsEarnings before interest and taxesProfit (economics)BusinessQuarter (Canadian coin)Time lagGross profitNet profitMacroMarketingLagAdvertisingDemographic economicsAccountingEconomics
DOInot available

Abstract

fetched live from OpenAlex

This study examines the time causal relationship between the operation profit of the listed fashion companies and the macro variables. Operating profit data of 36 listed fashion companies from 2000 to 2017 has been used. Macro variables include household income, household expenditure, number of Korean overseas travelers, number of foreigner travelers and sentiment index. The study results are as follows. First, the number of outbound travelers from Korea has a negative effect on the operating profit of listed fashion companies; however the number of foreigner visiting Korea has a positive effect at 0 time lag. Second, the consumer sentiment index had a positive effect on the sales and the operating profits of the listed fashion companies with a time difference between the 3rd and the 4th quarter. Third, a disposable income has a positive effect on the operating profit of listed fashion companies. Last, educational expenses have a negative effect on operating profit with a time lag between the first and the second quarter. The findings can be used as useful information to analyze the fashion industry and help fashion companies improve their financial performances.

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.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0270.031

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 head0.059
GPT teacher head0.257
Teacher spread0.199 · 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