MétaCan
Menu
Back to cohort
Record W2724769843 · doi:10.1142/s0218927517500043

Reliance Communications Ltd.: A House of Cards?

2017· article· en· W2724769843 on OpenAlexaboutno aff
S.R. Vishwanath, Vijaya Narapareddy

Bibliographic record

VenueAsian Case Research Journal · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicIndian Economic and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsRestructuringShareholderShare priceFinanceLeverage (statistics)BusinessDebtStock (firearms)Debt restructuringCorporate financeFinancial servicesEconomicsMarketingStock exchangeCorporate governance

Abstract

fetched live from OpenAlex

Reliance Communications Ltd., a large, Indian telecom company has experienced severe financial difficulties brought on by poor management practices, an ill-conceived capital expenditure of more than $6 billion, a debt load of $5.8 billion, and changing industry dynamics. Interestingly, two analysts at a Canadian Investment Research firm gave a “sell” recommendation on the stock with a target price of INR 15 when the stock was trading at INR 93. The analysts also pointed out a possible rip-off of minority shareholders by the firm’s founders. The company’s stock price fell from a high of INR 844 in 2008 to INR 46 in 2012. The company is in the process of restructuring its assets and liabilities. The case considers the economics of the business from the perspective of shareholders. Students are required to assess the company’s performance and decide whether they would invest in the company’s shares. The first half of the case describes the company’s constituent businesses and the strategy followed by the wireless division. This gives students an opportunity to critique RCL’s marketing strategy and pricing of its services. The second half describes the company’s financial strategy, the instruments it issued and its restructuring initiatives. The case allows students to examine problems associated with high leverage and the challenges to restructuring in emerging 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.644
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.203
GPT teacher head0.382
Teacher spread0.179 · 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.

Study designTheoretical or conceptual
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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAsian Case Research JournalSame topicIndian Economic and Social DevelopmentFrench-language works237,207