Formal Lifelong E-Learning for Employability and Job Stability During Turbulent Times in Spain
Bibliographic record
Abstract
In recent decades, international organizations have developed initiatives that incorporate lifelong learning as a tool to increase the employability of citizens. In this context, the goal of this research is to test the influence of formal e-learning on estimating employment status. The research made use of a sample of 595 citizens in 2007 and 1,742 citizens in 2011, using microdata from Eurostat's Adult Education Survey (AES) implemented by the Spanish Statistical Office [Instituto Nacional de Estadística] (INE) in Spain. Controlling for socio-demographics and formal education-level information, multiple binary logistic and ordinal regression models on formal education activities are used to check the separate effects of independent variables and demonstrate that Spanish people who have done formal lifelong e-learning activities are more likely to have an employment contract: i) in 2007, before the start of the economic crisis, for all individuals; ii) in 2011, during the economic crisis, for all individuals; iii) in 2011, for individuals with any level of computer literacy; iv) in 2011, for individuals whose highest education level is primary, secondary, or post-secondary non-tertiary; and v) in 2011, for individuals having more stable employment contracts, understood as a combination of duration (temporary, permanent), and working hours (part-time, full-time). Consequently, after inferential judgements based on the empirical results, it is shown that one of the most important factors for estimating employability in times of economic crisis has to do with lifelong e-learning. Moreover, formal e-learning activities can be a strategy for obtaining better job stability.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".