19. Digital Enlightenment: The Myth of the Disappearing Teacher
Bibliographic record
Abstract
This paper argues that the emerging post-print digital culture of knowledge creation and dissemination in higher education is even more demanding of effective and committed teaching than hitherto. This may run counter to a widespread view that the digital environment reduces the need for a strong culture of teaching, to be replaced by an educational culture of independent, self-sufficient learners. However, evidence for the precariousness of this outlook is provided by many recent reports in the United Kingdom that have illustrated how the assumptions of a ‘digital natives’ perspective on students and academics are largely inaccurate. While acknowledging the phenomenal expansion of the cultural horizon that has been afforded to students and academics in the post-print digital environment of university learning, the crucial role of the academic in the creative use of digital technology in teaching should not be underestimated, or higher education may be rendered incapable of supporting effective learning. To substantiate this viewpoint the paper presents preliminary data from a small-scale pilot survey of the take-up of information and communication technology (ICT) for teaching in our own School of Education.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".