Labour Supply/Demand Analysis: Approaches and Concerns (The Case of “Finance” Graduates in Albania)
Bibliographic record
Abstract
The tertiary education systems of Albania have expanded rapidly. This has had important and profound impacts on labour markets and in the way in which employers use highly educated labour. From the existing data we can say that still in 2015 we have an oversupply of graduates, which cannot be absorbed by labour market. On the other side, one cannot find the same tendency on the demand curve. Is there any possibility to match the two sides of the market? Are there figures to help decision makers think of any change in the actual education and employment policy? After extensive enquiries on literature, it’s concluded that there is no national data on demand and/or supply in the labour market in any profession in Albania. The study is mainly focused on: a) the calculation of the labour market demand for high-profile finance positions in Albania; b) calculation of the supply, defined as those majored in “Finance” at bachelor level in Albania; c) comparing the demand and supply curve. The main emphasis is put on presenting estimations of occupational mismatch for the “Finance” graduates in Albania. It concludes with some recommendations addressed to the respective target groups and other stakeholders involved in higher education sector in Albania. DOI: 10.5901/mjss.2016.v7n3p331
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".