MétaCan
Menu
Back to cohort
Record W3010782315 · doi:10.22367/jem.2020.39.01

The market of major film distributors in Poland in 2002-2018

2020· article· en· W3010782315 on OpenAlexaboutno aff
Aleksandra Bartosiewicz, Agnieszka Orankiewicz

Bibliographic record

VenueJournal of Economics and Management · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicArt History and Market Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMovie theaterDistribution (mathematics)OriginalityDominance (genetics)European marketMarket shareStudioMarket concentrationMarket structureBusinessMarketingCommerceIndustrial organizationSociologyEngineeringTelecommunicationsArtVisual artsSocial science

Abstract

fetched live from OpenAlex

Aim/purpose -The purpose of the paper is to describe and analyse the functioning of the cinema distribution market in Poland in 2002-2018. Design/methodology/approach -The results of the quantitative research of the fifteen major film distributors operating in Poland in the analysed period are presented in the paper, together with market capacity, market share ratios and measures of market concentration. Findings -Thanks to the analysis, large distributors operating in the Polish film market are characterised. The analysis of the structure and the concentration of the sector in question shows that nowadays over half of the cinema distribution in Poland is concentrated in the hands of four companies (UIP, Kino wiat, Monolith and Disney), two of which (UIP, Disney) are foreign branches of the major American studios. Thus, the results of the research provide empirical evidence on the impact of international distribution consortia on the Polish cinematographic industry. They are similar to the existing literature findings which concern other countries (e.g. Canada, New Zealand, Western Europe). This means that Poland is part of the trend of dominance of American distributors on global domestic markets.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.175
Teacher spread0.160 · 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 designNot applicable
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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Economics and ManagementSame topicArt History and Market AnalysisFrench-language works237,207