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Record W3212887080

한국과 캐나다 조직간 원격근무 수용태도와 기대효과에 대한 비교 연구

2017· article· ko· W3212887080 on OpenAlexaboutno aff
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Bibliographic record

VenueJournal of Information Technology Applications and Management · 2017
Typearticle
Languageko
FieldSocial Sciences
TopicEnergy and Environmental Systems
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessWork (physics)MarketingEmpirical researchQuality (philosophy)Public relationsPolitical scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

This research conducted empirical and comparative study following interview concerning the relationship of acceptance attitudes and expected effects of the teleworking between Korean and Canada organizations. Independent variable was acceptance attitude, and the expected effect as dependent variable. Totally, 201 responded questionnaires (Korea : 118, Canada : 83) were analyzed for multiple regression and mean difference between groups. On January 1, 2015, Canada had agreed FTA (free trade agreement) with Korea. Therefore, many organizations can have opportunities for sharing hands in social infrastructures and business area. As a result, the research found out that teleworking can positively improve employee’s quality of life, efficiency of works. Moreover, respondents informed us that it may give us national and social cost saving. The BYOD (bring your own device) will be helpful to make employees do more active communication. This comparative research expects that two countries have some insights to cooperate in smart work or teleworking. In addition, several Korean companies can have chances to export IT technologies to Canada market.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.985
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.006
GPT teacher head0.248
Teacher spread0.242 · 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
Published2017
Admission routes1
Has abstractyes

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