MétaCan
Menu
Back to cohort
Record W4388399740 · doi:10.5539/hes.v13n4p136

Doing a PhD in a Low-Income Country: Motivations and Prospects

2023· article· en· W4388399740 on OpenAlexvenueno aff
Gabriele Griffin

Bibliographic record

VenueHigher Education Studies · 2023
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsnot available
FundersUppsala Universitet
KeywordsScholarshipContext (archaeology)InstitutionDeveloping countrySociologyLow incomePublic relationsPsychologyPolitical scienceEconomic growthEconomicsSocial scienceSocioeconomics

Abstract

fetched live from OpenAlex

The purpose of this article is to analyse the gendered motivations of students to undertake doctoral research in a low-income country (LIC), Mozambique. Most research on PhD student motivation is done in high-income countries where the drivers for doing a PhD are quite different from those of people living in LICs. Drawing on original empirical research in the form of semi-structured interviews with PhD students from Mozambique, and utilizing the concepts of 'altruistic' and 'self-concerned' motivations, this article argues that context is a powerful determinant of motivation. The findings of the research highlight the need for scholarships as a major driver for undertaking a PhD in an LIC. Further, PhD students' motivations, unlike those in high-income countries where the self is at the heart of decisions to do a PhD, include altruistic motives such as the desire to serve one's country, institution, community, and people as well as having a voice in the public sphere. These altruistic motivations are more important than the more self-referential factors such as 'intrinsic interest in the subject' and 'self-fulfillment' that dominate the literature from high-income countries. This implies that donor countries, the common suppliers of scholarships for PhD students in LICs, need to ensure that scholarships are adequate to enabling PhD students from LICs to complete their degrees both in terms of duration of scholarship and in terms of amount. Without this completion rates are likely to be slow and low. The article calls for more research on this issue in LICs.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.273
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.234
GPT teacher head0.543
Teacher spread0.309 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueHigher Education StudiesSame topicDoctoral Education Challenges and SolutionsFrench-language works237,207