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Record W7132344827

Amazon’s DNA Driving Technological Innovation in the Digital Economy

2018· other· W7132344827 on OpenAlexaff
Xiaoming Zhu, Wenyin Qian, Tianyu Shi, Yifan Zhu

Bibliographic record

VenueCEIBS Institutional Repository · 2018
Typeother
Language
Field
Topic
Canadian institutionsCentre Casa
Fundersnot available
KeywordsDigital economyKnowledge economyKey (lock)Technological changeInformation economyNew economy
DOInot available

Abstract

fetched live from OpenAlex

成立20年,亚马逊定位从最初“地球上最大的书店”到“最大的网络零售商”再到“最以客户为中心的企业”,业务线不断扩大,从在线图书销售到品类扩张,再到仓储物流、第三方平台、Kindle生态系统、云计算、流媒体、智能家居等领域,2016年末又开设了线下实体零售店,从颠覆线下到回到线下。这其中业务的内在联系是什么呢?此外,亚马逊几乎处在微利和亏损状态,但估值却一路飙升至近4,000亿美元。为什么“不赚钱”的亚马逊会成为资本市场的宠儿呢?这家具有远见的公司背后的商业逻辑和商业趋势是什么?

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)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.051
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.007
Scholarly communication0.0100.008
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0510.004

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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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