Social Discourse, Comfort Zones and the Globalising World: South African Indian Emigrant and Resident Doctors on the Need to Migrate
Bibliographic record
Abstract
This paper is about conversations and opinions of Indian medical personnel who are working outside and inside South Africa.It covers the views of two groups: of medical general practitioners and medical specialists.The first group, with whom interviews were done as casual conversations individually and collectively, were employed outside the country.They were in the age group beyond 60 years of age, while the group inside the country were below the age of 40 years and were represented by the individual interviews with their close relatives.While the older group saw themselves as being bestowed with the twin advantages of experience and wisdom, the younger often articulated feelings that were in juxtaposition to one another.They expressed their appreciation for the comfort zones in which they were established but felt at odds with themselves when they compared their situations with their counterparts working in other countries, especially in the Middle East and the developed English speaking countries (especially the "big five": USA, Canada, UK, Australia and New Zealand).However settled, and no matter how they felt about themselves, the conversations with the members from each group were fraught on both sides with degrees of ambivalence, regret, success and confidence about their futures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.025 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".