“Brain Drain” or “Brain Gain”? Students’ Loyalty to their Student Town: Field Evidence from Norway
Bibliographic record
Abstract
In the global economy regions fight a two-front “war” to attract young people. On the one hand, they compete against more urban areas because young people leave home to study and do not return to their home region (“brain drain”). On the other hand, they struggle to attract new residents, students and entrepreneurs to their local region (“brain gain”). The context is a student town of a strong industrial region characterized by a net export of young people and an increasing demand for highly qualified labour. The purpose is to gain insight into how student loyalty to a student town may be enhanced. A partial least square path modelling approach is used to estimate a structural equation model of student town loyalty. One finding is that the creation of student town satisfaction has more influence on student town loyalty than reputation building. “Social activity” is the most important loyalty driver. This antecedent is mediated through student town satisfaction and reputation, as well as university college reputation. The town municipalities and the university college should thus be coordinated in their effort to increase student town loyalty to bring down the “brain drain” and increase the “brain gain” in the region.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".