Turning Brain Drain into Brain Gain: Harnessing Pakistan's Skilled Diaspora
Bibliographic record
Abstract
Leaders in developing nations are increasingly concerned about the economic impact of losing highly qualified citizens to opportunities in more developed countries, particularly countries in the West. This diaspora phenomenon, often labeled brain drain, refers to developing countries losing intellectual capital through the emigration of highly skilled individuals. To understand brain drain, the objective of this exploratory descriptive and interpretive study was to explore the various conditions under which Pakistani diaspora settled in the Greater Toronto Area (GTA) would be willing to engage in knowledge transfer activities. Theoretical frameworks of reciprocity and human capital theory grounded this study. The guiding research question was: What are the conditions under which diaspora members would be willing to engage in knowledge transfer activities with Pakistan? The study used an exploratory descriptive research design with an interpretive approach to examine leveraging diaspora engagement to convert brain drain to brain gain in Pakistan. Interviewing diaspora was essential to determine how developing countries, like Pakistan, can harness the knowledge of its diaspora for developmental purposes. A total of 15 face to face interviews of Pakistani diaspora who settled in the Greater Toronto Area were conducted for this study. Based on the findings of the study, Pakistan has a significant opportunity to tap into its more than seven million diaspora (Haq et al., 2013) and engage them for its capacity building purposes. Findings from the study indicated diaspora knowledge transfer is dependent on life stages and years spent in a new country. Individuals new to a country are less likely to engage in knowledge transfer than those who have lived in a new country for a longer duration. To achieve success in engaging with Pakistani diaspora, factors like mutual trust, a need to approach diaspora engagement in a structured manner and more importantly a need to have a broad vision on the part of sending countries were considered essential requirements to achieve a successful diaspora engagement strategy. There is a need and an opportunity for Pakistan to reflect and transform its thinking about its diaspora and the developmental role they might play beyond sending remittances.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| 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".