Distance Education – Global Issues 2 DISTANCE EDUCATION IN THE PERSPECTIVE OF GLOBAL ISSUES AND CONCERNS
Bibliographic record
Abstract
The history of international development is more than 50 years old. The origin of its pre-history may be located hundreds of years earlier, when the efforts of navigators and new conceptualizations by scientists started changing our idea of the world and of our place within it (e.g. Boorstin, 1985; Koestler, 1959). Those who had the economic power and who therefore had access to the technology of the day, discovered that they were not alone in the world and that other peoples – for a long time seen as essentially different and invariably inferior – co- inhabited the planet. Different forms of cohabitation, often of an exploitative nature, emerged in the process of colonization that followed. That period came to an end during the third quarter of the last century. The movement towards emancipation and decolonization, largely driven by the formerly oppressed, led to the recognition among those who eventually relinquished power that not everything in the world was right. In fact, it brought to the forefront that there were great inequalities that conflicted with long held moral convictions – convictions that had, until then, been solely applied (and even then only partially) to the societies to which those who held the
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".