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Record W4411576331 · doi:10.70886/ujer.25131.007

School Resources as Management Motivation Strategies on Teacher’s Job Performance in Secondary Schools in Katsina Zonal Education Quality Assurance Katsina State, Nigeria

2025· article· en· W4411576331 on OpenAlexfundno aff
Mohammed Awwalu Usman, Dahiru Ahmad Muhammad

Bibliographic record

VenueUMYU Journal of Educational Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
FundersUniversity of TorontoUniversity of Cambridge
KeywordsQuality assuranceState (computer science)Quality (philosophy)PsychologyMedical educationPolitical scienceBusinessMedicineMathematicsPhysicsMarketing

Abstract

fetched live from OpenAlex

The study investigated the school resources as management motivation strategies on teacher’s job performances in Katsina Zonal Education Quality assurance in Katsina State, Nigeria. The study has two objective, two research question was raise in line with the stated objective and one null hypothesis was formulated for test at 0.05 level of significance. The research design was descriptive survey research design. A sample of 291 teachers was selected from the population of 1443 of 25 public Senior Secondary School using simple random sampling techniques. A self-designed questionnaire was used for data collection. The questionnaire items were rated using a four point likert scale of measurement. SPSS version 23.0 was used and the data obtained was analysed using PPMC and multiple regression analysis at 0.05 level of significance. The finding showed that, there is significant relationship between school resources as indices of management motivation strategies on teachers Job performance in Katsina State. The study recommended that: provision of adequate school resources enhance teachers Job performance in public Secondary School in Katsina State.

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)0.007
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.279
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.467
Teacher spread0.394 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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