Educational structures in context: at the interface of higher education
Bibliographic record
Abstract
The editors have grouped the chapters into three sections, each dealing with more general or more specific issues pertaining to education. The first section introduces chapters that deal with general questions about the existing structures of formal academic education. While the authors adopt a critical approach to the existing structures of formal Higher Education, they seem to agree that the primary role of university in creating and organising the social life, (helped by the personal example and experience of the teacher), not only should not be ignored, but should be reinforced to protect universities against the reigning opportunism, managerialism, and bureaucracy that seem to have been affecting, in various degrees, all modern structures. The second section of the volume focuses on more specific issues concerning Higher Education in different national contexts. While the challenges that Higher Education faces in Canada are different than the challenges it faces in Pakistan or Turkey, the authors agree on one thing: Higher Education should promote the values of democracy and tolerance, prepare students not only for the labour market but also for active citizenship. Historically, universities have been hubs of creativity. In the context of globalisation where national and cultural boundaries are questioned, universities should resume (or keep) their role of facilitators of social change. This entails the need for Higher Education to remain an autonomous social institution and be available to all social groups, including, and especially, the ones whose access to higher education have been traditionally restricted. The third section focuses on the impact of technology, and more specifically, the Internet, on Higher Education. The issues the authors tackle range from the use of Web 2.0 technology to the use of mobile phones and different online platforms during lectures. Despite the very different issues and goals that the authors see with regard to the use of technology in the classroom, they argue along the same lines: the fast access to detailed and voluminous information, and the effective structuring of this information that technology offers, should be incorporated as a useful tool in any classroom, to help the lecturer deliver the material successfully and remain connected to the students (and successfully resolve specific problems that students might face with the course material).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".