Selvitys toisen tai useamman korkeakoulututkinnon suorittaneista
Bibliographic record
Abstract
This brief provides descriptive evidence on completion of multiple higher education degrees in Finland. In the year 2020, there were altogether 7,100 individuals enrolling in higher education who already had higher education degrees. A quarter of them already had more than one higher education degree. The share of those with prior degrees participating in higher education has been increasing sharply during the past two decades. In the year 2020, ten percent of all new students in higher education had already completed higher education previously. The most common education choice among these students was the field of Health and Welfare in Universities of Applied Sciences. According to the analysis, students with prior degrees took almost as long to complete the degree when compared to those who were completing their first degree. Women and those whose mother tongue is Finnish are vastly over-presented among those completing multiple degrees. Additionally, these students are performing fairly well in the labor market before returning to higher education. Completing multiple degrees does not lead to higher income or better employment even in the medium term. There is a drop in the labor market outcomes following the new enrollment in higher education; however, they bounce back to their previous level in a few years. After that, the labor market performance follows a very similar trend to what it was before enrollment.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".