Reinventing the teaching-research nexus to foster university education in the twenty-first century
Bibliographic record
Abstract
This forum paper first sheds light on the strains on the teaching-research nexus derived from neo-liberal globalisation, neo-nationalism and the impact on higher education (HE). Then, it asserts that the evolving configurations of priority among teaching, research, and transfer activities in HE have resulted in a fragmentation and stratification of roles or identities in academic profession, and essentially rendered the teaching-research nexus to fall apart. One response, the German case of real-world labs, prompts academics to work together with practitioners and engage in problem-based approach to teaching and learning. The China case of initiatives leaning back to reinforcing education and teaching demonstrates an attempt of reverting to the basics in order to reboot universities against the context full of changes and uncertainties. Finally, it attempts to reinvent Clark’s ‘research-teaching-study nexus’ with enriched and upgraded analytical capacity in capturing the complexities and nuances in the new and globalizing 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 | 0.023 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| 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".