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Record W4307452599 · doi:10.57185/joss.v1i2.25

The Effect Of Return On Asset (Roa) And Dividend Policy On The Value Of Manufacturing Companies

2022· article· en· W4307452599 on OpenAlex
Nunuk Novianti, Indra Wijaya Indra Wijaya, Ani Febianingsih Ani Febianingsih

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Social Science (JoSS) · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Analysis and Corporate Governance
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsStock exchangeNonprobability samplingBusinessReturn on assetsIBMPopulationDividendData collectionAccountingFinanceStatisticsMathematics

Abstract

fetched live from OpenAlex

The purpose of this research is to know how much influence of Return on Assets (ROA) and Dividend Policy to Firm Value partially on Manufacturing Companies. The method of data collection is through the population data of manufacturing companies for the period 2017 to 2021. The data is taken through the official website of the Indonesia Stock Exchange (www.idx.co.id). The total population was found to be 167 listed companies. The samples of companies used in this study are twenty seven companies that have passed the criteria that have been determined in purposive sampling. In this study, the data used is secondary data. To obtain secondary data, it is taken from financial reports, annual reports and sustainability reports of each manufacturing company listed on the Indonesia Stock Exchange. Data processing using IBM SPSS 25 application.

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.

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)0.003
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 score0.889
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.232
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