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A critical study on the financial performance of selected mobile operators in India

2025· article· en· W4414185637 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Financial Management and Economics · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBanking Sector Performance and Management
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Financial servicesCustomer baseCore (optical fiber)Mobile telephony

Abstract

fetched live from OpenAlex

India is the second-largest telecoms market globally. As of January 2021, the overall subscriber base in the nation reached 1,183.49 million, and the gross income of the telecom industry was US$ 9.35 bn in the 3rd quarter of the financial year 2021. Over the last five years, several challenges have confronted small enterprises that have been severely impacted by the dominant telecom leader. Financial performance is a subjective assessment of a firm's ability to use assets from its core business operations to create income. This item serves as a comprehensive indicator of a firm's financial health over a certain timeframe and may facilitate comparisons across comparable enterprises within the same industry or across other industries or sectors collectively. This study analyses the financial performance of the Indian telecom industry, specifically focusing on BSNL, Airtel, and Vodafone. The Indian telecom industry significantly contributes to the growth of our nation and other industries.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.007
GPT teacher head0.225
Teacher spread0.218 · 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