Bibliographic record
Abstract
To cite this article: Dr Ingrid Harrington. (2022). Message from the Editor-in-Chief. International Journal of Higher Education, 11(5), p0-1. https://doi.org/10.5430/ijhe.v11n5p0 doi:10.5430/ijhe.v11n5p0 URL: https://doi.org/10.5430/ijhe.v11n5p0 We continue to be confronted by ongoing global challenges that require educators and students to conform to learning that requires greater patience, application, flexibility, and a willingness to learn differently. It would appear that no corner of the globe is exempt from the implications to their daily life of the actions and relationships between countries. Years on, the full ramifications of the COVID-19 pandemic are still evident in the practices and policies of tertiary educators, as evidenced by the many articles that continue to discuss their experiences. What has remained a constant for higher education educators is their duty to ensure that they provide opportunities for their students to develop and experience a sense of belonging, community, and ‘place’ in the virtual classroom. Another challenge is ensuring that the design of educational offerings taps into and nurtures the development of student learning styles and approaches to learning, that can lead to a successful, meaningful, productive and enjoyable student learning experience.The IJHE is proud to provide an avenue for researchers to share their findings to enhance the overall student experience. We are proud to present this issue with 16 contributions from Thailand, South Africa, Israel, Australia, Malaysia, Canada, the USA, Switzerland, Oman, Korea and China. This issue has a strong focus on improving the academic delivery and access of information to students in higher education. Research on information and communication technology deficits and innovations, assessment techniques, balancing home, study and employment, and leadership, will provide interesting and informative reading for all.
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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 | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.097 | 0.097 |
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".