Higher Education and the Debate on Key/Generic Skills
Bibliographic record
Abstract
This article addresses current questions about the importance of key/generic skills in higher education, based on a Meta-evaluation methodology. It is argued that key skills are a matter of debate among educators and other researchers in the neo- and post-Ford economy. The article also analyzes questions that relate to the rationality of key/generic skills, such as whether these skills are occupationally or professionally specific, whether they are professionally or organizationally specific, and how they can be transferred or taught in higher education. The authors’ findings reveal that, first, key skills are specific to particular social domains and, second, there are strategies in line with Bridges’s distinction of transferable and transferring skills that can be employed to transfer key skills. Also with regard to key/generic skills, the authors assert that there are ranges of preparatory work to be done in higher education or other educational institutions and that fluency can only be achieved through practice in specific contexts. The limitation of these findings is that there remains a high degree of indeterminacy because the “generic” elements that are taught in higher education must still be applied in a wide range of different contexts.
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 | 0.260 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| 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".