Bibliographic record
Abstract
In an age seemingly defined by near-ubiquitous access to Internet-based communication, there is little wonder that adult educators are turning to online, distance education as a means to reach their participants. In the traditional academy, post-secondary institutions increasingly include online courses and programs as elements, or comprising the entirety, of both undergraduate and graduate degrees (Allen & Seaman, 2006). Even in the realm of non-formal adult education, “hacktivism1” has become one of the most effective mechanisms through which engagement for social change – especially on a global scale – occurs (Day, 2004; Ganesh, Zoller & Cheney, 2005). Ironically, rather than truly integrating the philosophy of emancipatory and transformative adult education, cyber-education environments as typically implemented throughout the academy, overwhelmingly – if unwittingly – reproduce and reinforce the hegemony of traditional teacher-pupil power relations. By examining the mechanism of hegemony, and its pervasive presence in contemporary pedagogical technologies, this chapter will demonstrate how organized power is maintained through these mechanisms. In contrast, a case will be offered that demonstrates how engaged intellectuals can reconstruct the cyber-education environment in order to challenge the pretensions of entrenched academic power, and manifest adult education principles. In particular, the case will explore how the many years of research on how adults learn can be applied with the use of technology, so that the cyber learning milieu is as dynamic, personal and collaborative as the physical presence classroom context can be in the hands of a skilled adult educator.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.081 | 0.032 |
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".