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Record W3164276766 · doi:10.52634/mier/2017/v7/i2/1417

STRATEGIC MANAGEMENT IN INDIAN HIGHER EDUCATION

2021· article· en· W3164276766 on OpenAlexaboutno aff
Sahil Sawhney, Kulwant Kumar, Ankur Gupta

Bibliographic record

VenueMIER Journal of Educational Studies Trends & Practices · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Leadership and Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsChinaGovernment (linguistics)Higher educationBusinessPolitical scienceState (computer science)Economic growthPublic relationsPublic administrationEconomicsComputer science

Abstract

fetched live from OpenAlex

Strategic Management (SM) finds wide adoption across the industry, government, military academia and other organizations with a view to ensuring survival and longterm growth. Universities across the world, especially in the USA, UK, Australia, Canada and China, have successfully implemented SM to build strong international brands and consistently maintain top global rankings. Indian Higher Education (IHE) sector, however, has not been proactive in adopting SM to address the numerous challenges that confront it. In this paper, the authors have explored the current state of SM adoption in IHE, highlighting the challenges in its implementation and suggest some ideas to help institutionalize SM for effecting long-term positive change in the sector.

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 categoriesInsufficient payload (model declined to judge)
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 score0.763
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.116
GPT teacher head0.363
Teacher spread0.248 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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