En expertgrupps syn på livslångt lärande inom högre utbildning : Förändringsbehov och vidareutveckling
Bibliographic record
Abstract
Det finns ett behov av kontinuerlig kompetensförstärkning och här spelar högre utbildning en viktig roll. Medan de traditionella universitetsutbildningarna ofta är utformade för studenter i tjugoårsåldern, kräver dagens kunskapssamhälle ett livslångt lärande för ett vidare åldersspann. Utifrån denna utgångspunkt genomförde Peter Mozelius, Marcia Håkansson Lindqvist och Jimmy Jaldemark vid CER, tillsammans med Martha Cleveland-Innes vid Athabasca University i Kanada, en studie där resultat från enkäter och intervjuer med en internationell expertgrupp indikerar en rad olika förändringsbehov. I denna kortrapport presenteras de aspekter som framkom i form av en konceptuell modell. Forskningsresultaten har tidigare presenterats vid två internationella konferenser: “Digging deeper with Delphi: The four step Alberta approach” (Mozelius, Cleveland-Innes, Håkansson Lindqvist och Jaldemark 2023a) och “The transition of higher education for continuous lifelong learning: Expert views on the need for a new infrastructure” (Mozelius, Cleveland-Innes, Håkansson Lindqvist och Jaldemark 2023b) samt i tidskriftsartikeln ”Critical aspects of a higher education reform for continuous lifelong learning opportunities in a digital era” (Mozelius, Cleveland-Innes, Håkansson Lindqvist och Jaldemark 2024).
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.013 |
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".