Technological Distance: A New Way of Conceptualizing the “Distance” in Distance Learning
Bibliographic record
Abstract
This paper presents the concept of technological distance, which describes a gap between technologies (broadly defined to include methods, tools, principles, and processes) available for a learner to learn, and those needed to complete that learning. This gap is a measure of both the participation and autonomy of learners in the process. Available technologies may include those provided by teachers and institutions as well as from many others, most notably including the learners themselves. The technologies that make up the assembly—pedagogies and regulations as much as digital devices or classrooms—are only components, however. What matters most is how learners fill the gaps between them to achieve their learning goals. These are fundamentally situated, idiosyncratic, and human. Technological distance provides new insights into other models that use spatial metaphors, such as the presences of communities of inquiry and transactional distance.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.010 | 0.027 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".