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Record W4288749542 · doi:10.5430/ijba.v13n4p72

Research on the Development Status and Strategies of Micro Web Series in China

2022· article· en· W4288749542 on OpenAlexvenueno aff
Ling Jiang, Ruoxuan Wang

Bibliographic record

VenueInternational Journal of Business Administration · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicE-commerce and Technology Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsComputer sciencePopularityWeb developmentMaturity (psychological)Quality (philosophy)World Wide WebMultimediaThe Internet

Abstract

fetched live from OpenAlex

In the era of "micro", the popularity of short video platforms provides a reasonable basis for developing micro web series, and the fast-paced life makes users more inclined to receive the piecemeal audio-visual experience. Along with the complement of network audio-visual industry developing planning and policies, the improvement of content audit system, the maturity of streaming technology, and the drive for content innovation, China's micro web series market has shown vigorous development. The short video platforms have provided distribution channels for micro web series, the comprehensive video platforms have joined to make the micro web series boutique, and the theatrical operation and support programs of platforms have provided a standardized and orderly market environment for micro web series. However, there are still shortcomings in the quality of episodes, policy regulation, cashing mode, content depth, and marketing channels. Micro web series is still relatively new, and little research has been conducted in this field. Therefore, it is necessary and innovative to study the development status of the micro web series. Meanwhile, this study puts forward development strategies such as branding and quality construction of micro web series to promote the healthy development of China's micro web series industry.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.892
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.325
Teacher spread0.276 · 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 teacher head, 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
Published2022
Admission routes1
Has abstractyes

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