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Record W3122020906 · doi:10.5430/rwe.v12n2p113

Remuneration Reforms and Welfare of Employees in Public Schools: Experience From Nigeria

2021· article· en· W3122020906 on OpenAlexvenueno aff
Akabom Ita Asuquo, Arzizeh Tiesieh Tapang, Glory Tony Effiong, Mkpa Ubuo Linus, Ashishie Peter Uklala, Sylvester Bassey Duke

Bibliographic record

VenueResearch in World Economy · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicScheduling and Timetabling Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsRemunerationWelfareConsumption (sociology)EarningsBusinessGovernment (linguistics)EconomicsLabour economicsAccountingFinance

Abstract

fetched live from OpenAlex

The survey was undertaken to ascertain the impact of remuneration reforms on the workers’ welfare in public schools taking evidence from Nigeria. Facts were gotten by the use of a planned inquiry form and the data collected were then evaluated by means of the multiple regression techniques. Outcomes showed that there is a substantial impact of indicators of remuneration reforms on the welfare pointer (consumption) of employees in public schools. The negligible inclination to consume of employees with minimum remuneration, as well as employees with excessive remuneration, had revealed that these two categories put apart a huge percentage of their earnings for consumption to improve their welfare status and satisfaction level. Actions suggested to augment employees’ wellbeing and propensity comprise of periodic remuneration rising assessment, regulation of price increases ratio in addition to setting up of nutrition subsidizations for employees in public schools by the 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)0.001
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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.298
GPT teacher head0.460
Teacher spread0.162 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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