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Record W1606828606 · doi:10.5539/ies.v8n6p194

Income Generation Activities among Academic Staffs at Malaysian Public Universities

2015· article· en· W1606828606 on OpenAlexvenueno aff
Abd Rahman Ahmad, Ng Kim-Soon, Ngeoh Pei Ting

Bibliographic record

VenueInternational Education Studies · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
FundersUniversiti Tun Hussein Onn Malaysia
KeywordsHigher educationCommercializationSustainabilityQualitative researchPublic universityPublic relationsOrder (exchange)BusinessLow incomeMarketingEconomic growthSociologyPolitical scienceEconomicsPublic administrationSocioeconomicsFinanceSocial science

Abstract

fetched live from OpenAlex

Income generation activities have been acquainted among public higher education institutions (HEIs) in Malaysia. Various factors that brought to insufficient of funding caused Higher Education Institutions(HEIs) to seek for additional income as to support the operation expenses. Financial sustainability issues made up the significant impact towards HEIs. Through the different instruments adopted by HEI, perhaps academic staffs are one of the parties that in charge of the income generation at universities. This research employed qualitative method by conducting interviews as a medium to provide insights to researcher. Then, the interviews data are analysed using the Interactive Model. The results pointed out that the main income generation activities originated from the research and consultancy whilst commercialization contributed the most significant income towards university. As a conclusion, the income which generated by the academic staffs is at upmost important to the development and sustainability of a university. Perhaps this research is significant to those who are concerning on the issues of income generating activities arisen among academic staffs. Through the results gained, certain parties may get known to the root of problems and then, solve it. It will eventually help the university to get a better way in order to attain the optimal results in income generation.

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 score0.392
Threshold uncertainty score0.617

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 communication0.0000.002
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 head0.073
GPT teacher head0.318
Teacher spread0.245 · 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

Citations13
Published2015
Admission routes1
Has abstractyes

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