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Record W2144932588 · doi:10.37119/ojs2011.v17i3.72

Teacher Recruitment and Retention in Select First Nations Schools

2013· article· en· W2144932588 on OpenAlexaffvenueabout
Robin Mueller, Sheila Carr-Stewart, Larry Steeves, J. D. Marshall

Bibliographic record

Venuein education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsUniversity of ReginaUniversity of Saskatchewan
Fundersnot available
KeywordsSalaryPolitical scienceAdministration (probate law)Consumption (sociology)Economic growthMedical educationPublic administrationMedicineSociologyEconomicsSocial science

Abstract

fetched live from OpenAlex

Historically, the inequitable funding for First Nations schools in comparison to funding for provincial schools has been an issue from the time of early day schools, to residential schools in which students worked half a day to tend to crops in order to grow food for consumption by students and staff, and to present day where band-managed schools still experience lack of funding. Similarly, the lower salary levels for teachers in federal and today in First Nations-managed schools has been identified as a significant issue related to teacher retention. The purpose of this research was to identify current factors affecting teacher recruitment and retention in present day First Nations’ managed schools. In this paper we report on one case study comparing funding and teacher retention in one provincial school system and schools in one Tribal Council in Saskatchewan.Keywords: funding; First Nations schools; administration; teacher recruitment; teacher retention

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)0.008
metaresearch head score (Gemma)0.024
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 score0.097
Threshold uncertainty score0.192

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.003
Scholarly communication0.0030.001
Open science0.0010.004
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 head0.058
GPT teacher head0.365
Teacher spread0.308 · 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

Citations14
Published2013
Admission routes3
Has abstractyes

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