The Predictive Value of Disruptive Technology Theory for Digital Publishing in the Traditional Publishing Environment: A South African Case Study
Bibliographic record
Abstract
Digital technologies such as e-books are predicted to have a profound effect on publishing, but they are yet to have a serious impact on the industry. This paper considers the implications of digitization and digital publishing for the trade book publishing industry in South Africa. Through surveys and interviews with South African trade publishers, a picture was developed of the current state of digital publishing. This state is evaluated using the context and predictive value of disruptive technology theory. In this case, digital technology is seen as a disruptive technology in the traditional print publishing environment. As the paper shows, the problems that publishers are experiencing are characteristic of industries faced with disruptive technology. The principles of disruptive technology can therefore be applied to develop recommendations and suggest strategies for publishers planning to venture into digital publishing. Although the focus of the research was on South African trade publishers, the results and recommendations that emerged from the research can be applied to different sectors of publishing as well as to the wider international publishing industry.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".