From Gutenberg's Galaxy to Cyberspace: The Transforming Power of Electronic Hypertext
Bibliographic record
Abstract
From Gutenberg's Galaxy to Cyberspace: The Transforming Power of Electronic Hypertext is the first empirically-grounded study of how hypertext transforms writing and thinking, and impacts literacy and education. In this ground-breaking work, Dr. Jean Mason uses the power of media-rich hypertext as the dynamic by which to explore the properties and possibilities of hypertext as a writing medium.\n\nFrom Gutenberg?s Galaxy to Cyberspace traces the experiences of a group of ?hyper-writers? as they struggle to master a new writing process that includes electronic links, visual images, sound, animation, and other forms of data within a single digitized writing space. Mason?s interpretations of collected data challenge educators to reframe their notions of literacy, points of reference, and roles as educators. Mason theorizes a new conceptual model for the hyper-writing process, suggests applications for pedagogy, and speculates about the far-reaching implications of non-linear ?hyper-thinking? in academic discourse and beyond. \n\nIn addition to the detailed discussion of its subject, this e-text archives both the study itself and a surrounding web of research materials. From Gutenberg?s Galaxy to Cyberspace is based on the first-ever web-based doctoral dissertation at McGill University in Canada, where it successfully challenged academic norms and expanded the perimeters of doctoral discourse. This study is an indispensable resource for scholars engaged in research related to technology, pedagogy, communication, and literacy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".