Alumni Networks—An Untapped Potential to Gain and Retain Highly-Skilled Workers?
Bibliographic record
Abstract
In times of increasing skills shortage, regions and particularly non-core regions, need to attract highly-skilled workers. It is better for these regions to (re)-attract highly-skilled workers that gained knowledge and contacts elsewhere and because they once lived in the region for study have already ties to the university region than trying to attract outsiders without such ties. In general, social networks can contribute to nurturing a “warm place” perception among potential workers. This paper looks at special kinds of social networks. It focuses on higher education alumni networks and discusses their role in retention and (re)-attraction for increasing the highly skilled workforce in their university regions. Being part of a university community means that alumni networks are able to maintain continuous contact with their alumni and have a positive effect on students remaining in the region. This can occur through co-operation with the regional economy. However, the current study found that the analysed alumni networks were set up primarily as communication instruments for graduates and alumni and not for regional economy purposes. If elements of retention and (re)-attraction are found in the network activities, this is more an unintentional side effect than a purposeful attempt to contribute to the regional economy. This paper argues that alumni networks could take on such a retention and (re)-attraction function if they broadened their scope of activities and reorganized their management structure. Alumni networks are an untapped potential, which can be activated for regional purposes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".