MétaCan
Menu
Back to cohort
Record W4200063068 · doi:10.46425/j119029034

Estimating the Financial Incentive for Caribbean Teachers to Migrate: An Analysis of Salary Differentials using Purchasing Power Parity (PPP)

2021· article· en· W4200063068 on OpenAlexaboutno aff
Gavin George, D. Rhodes, Christine Laptiste

Bibliographic record

VenueJournal of Education and Development in the Caribbean · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsnot available
Fundersnot available
KeywordsSalaryPurchasing power parityEarningsPurchasing powerDemographic economicsIncentiveEconomicsLabour economicsGeographyBusinessExchange rateFinance

Abstract

fetched live from OpenAlex

The teaching stock within the Caribbean region has been eroded by migration to developed countries. Higher potential earnings are one of the motivating factors to move abroad, but little is known about the extent of the income disparity between countries in the Caribbean and popular destination countries. Teacher salary comparisons are undertaken between selected countries in the Caribbean; Suriname, Trinidad and Tobago, St. Lucia, and Jamaica and popular destination countries, namely; United Kingdom, United States, and Canada using a purchasing power parity (PPP) exchange rate. Results show that newly qualified teachers can earn substantially more abroad, with Canada paying over twice the PPP adjusted salary compared to that offered in Jamaica (133.1%) and Suriname (110.6%). The United States offers the highest earning increases for mid- and late career teachers at over three times that offered in Jamaica (214.5%) and Suriname (223.4%). Canada is a close second across all Caribbean countries, whilst the United Kingdom offers the smallest salary differentials at 153.6% for Jamaica and 64.8% for St. Lucia. The study further reveals that there are salary disparities within the Caribbean, which may be a motivating factor for intra-regional migration.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.040
GPT teacher head0.365
Teacher spread0.325 · 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 designQualitative
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

Explore more

Same venueJournal of Education and Development in the CaribbeanSame topicEducation and Technology IntegrationFrench-language works237,207