Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".