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Educational leadership in the Niger Delta region of Nigeria: A study of the perceptions of its impact on the acquired leadership skills of expatriate Nigerian postgraduates

2009· book-chapter· en· W78555450 on OpenAlexaboutno aff
Gerald I. Akata, Jasmine R. Renner

Bibliographic record

VenueInternational perspectives on education and society · 2009
Typebook-chapter
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsNigeriansExpatriateNiger deltaPolitical scienceEconomic growthGeographySocioeconomicsSociologyDeltaEngineeringLaw

Abstract

fetched live from OpenAlex

Educational researchers have long experienced increasing rates of Nigerians educated to the graduate levels going overseas as a way to leave Nigeria. For the last 25 years, research has shown a rapid increase in the brain-drain syndrome in Nigeria (Akomas, 2006; Oji, 2005). From the history of expatriate Nigerians, research showed that the return rate of Nigerians who studied and obtained Ph.D.s in foreign countries shares a noticeable portion of the university educational outcomes and cannot be ignored. Pires, Kassimir and Brhane (1999), Oji (2005), West (2005), and Akomas (2006) agreed that brain-drain syndrome in Nigeria is increasing. Many Nigerian professors teaching in the universities in Nigeria have either gone overseas or are looking for ways to leave the country for greener pastures (West, 2005). In South Africa, one would find hundreds of Nigerian professors educating South Africans (West, 2005). Many are in Saudi Arabia, the United Kingdom, Holland, Germany, Canada, Australia, New Zealand, the United States, and many more places beyond the shores of Nigeria (West, 2005). Therefore, both educational leaders in the universities in Nigeria, in general, and Niger Delta region, in particular, and expatriate Nigerians educated to the graduate levels play a substantial role in the country's educational leadership effectiveness and success.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.088
GPT teacher head0.363
Teacher spread0.275 · 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 designQualitative
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

Citations3
Published2009
Admission routes1
Has abstractyes

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