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Record W4415586088 · doi:10.21083/crrf.v31i1.7316

The Work Experience of Teachers in Rural Canada: Examining Labour Market Conditions and Labour Mobility of Teachers in Rural Versus Urban Canada

2023· article· W4415586088 on OpenAlexaboutno aff
E. Dianne Looker, Ray D. Bollman

Bibliographic record

VenueProceedings of the Canadian Rural Revitalization Foundation · 2023
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsnot available
Fundersnot available
KeywordsCensusRural areaWork (physics)School teachersSurvey data collectionInstitutionWork experience

Abstract

fetched live from OpenAlex

The work experience of teachers in rural Canada: examining labour market conditions and labour mobility of teachers in rural versus urban Canada. Education is not only a key public institution it is central to the engagement and cohesion in many rural communities. One issue for rural communities is attracting and keeping teachers for their schools. This paper will review data from Statistics Canada to document the employment situation and the mobility of teachers in rural as compared to urban Canada. Specifically, it will use data from Canadian census and the annual Canadian Labour Force Survey to examine rural-urban differences and similarities in the trends over time in: the ratio of teachers to the school aged population; the number of teachers hired per year; the number of teachers with a permanent versus a temporary position, and the average tenure of employed teachers. Also presented will be data on teacher mobility in rural and urban areas, after one year and after five years. The paper will provide some key baseline data for understanding the labour market situation of teachers in rural as compared to urban areas of the country.

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)0.001
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 score0.047
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
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 head0.015
GPT teacher head0.223
Teacher spread0.208 · 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
Published2023
Admission routes1
Has abstractyes

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