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An Assessment of Housing Delivery in Nigeria: Federal Mortgage Bank Scenario

2012· article· en· W2116465586 on OpenAlexvenueno aff
Joseph K. Ukwayi, Eja E. Eja, Felix E. Ojong, Judith E. Otu

Bibliographic record

VenueCanadian social science · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsChristian ministryFinanceBusinessLoanGovernment (linguistics)Mortgage insuranceLoan-to-value ratioValue (mathematics)Mortgage underwritingFinancial systemPolitical science

Abstract

fetched live from OpenAlex

In recent times the federal mortgage bank spite of it role in housing delivery has recorded little or no success which is the major concern of this paper to critically assess the extent to which the federal mortgage bank has recorded success in housing delivery in Nigeria. Information on the extent of housing delivery was obtained from federal ministry of statistic and federal mortgage banks. However, findings indicate that in 2002 to 2005, the mortgage finance bank was able to mobilized N19.175 billion compared to 1992 to 2002 with a value of N11.451 billion showing a growth rate of 82%. It was discovered that the bank granted loan value of N4.531 billion to 4,151 national housing fund to contributors to either build or renovate their houses. Nevertheless, the mortgage finance bank has recorded little or no success but has appreciably improved in terms of fund mobilization which has aided increase in housing delivery in Nigeria. Key words: Housing delivery; Mortgage bank; Finance; Nigeria; Government

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.036
GPT teacher head0.334
Teacher spread0.298 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations9
Published2012
Admission routes1
Has abstractyes

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