MétaCan
Menu
Back to cohort
Record W4372060501 · doi:10.1051/shsconf/202316501001

An Investigation on the Streaming Industry: With the Case of Netflix

2023· article· en· W4372060501 on OpenAlexaff
Yao Yuan

Bibliographic record

VenueSHS Web of Conferences · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsSt. Stephen's University
Fundersnot available
KeywordsRentingBroadcasting (networking)AdvertisingLive streamingBusinessTelevision industryComputer scienceTelecommunicationsMultimediaEngineering

Abstract

fetched live from OpenAlex

Streaming is growing rapidly in the world, and streaming is gradually replacing television, radio and cinema. The American streaming company Netflix is one of them. In this paper, The author will explore exactly how the success of Netflix, a company that started out as a small DVD rental company, became the most successful streaming company in the world. Currently, Netflix is one of the most successful streaming companies. After Netflix, the film and television industry will not be the same. So it is important to study the company’s success and understand the technology and rules of broadcasting and Netflix business. By exploring Netflix’s success, the development of the film and television industry can be predicted in the next 10 years.

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)0.005
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 score0.088
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0130.006
Scholarly communication0.0090.009
Open science0.0010.004
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0110.001

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 head0.049
GPT teacher head0.248
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

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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueSHS Web of ConferencesSame topicFinTech, Crowdfunding, Digital FinanceFrench-language works237,207