Weakness exploitation: Predicting socially communicative devices as a successor to internet-based graphical user interfaces
Bibliographic record
Abstract
Existing theories of technology transitions cannot predict what new technological paradigm will supplant the currently leading, internet-enabled graphical user interface paradigm. This article introduces a preliminary approach (‘weakness exploitation’) to explain the rise and fall of four technology ‘empires’: print, television, the internet and socially interactive devices (such as robots, chatbots and internet of things devices). The approach is related to technology diffusion and disruptive innovation, but with a predictive element induced from Marshall McLuhan’s descriptions of print and television as ‘extensions’ of the senses. It is applied to the internet as an historical example of a technology transition outside of McLuhan’s original analysis and to explain why excessive exposure to screen-rendered digital media as the internet’s exclusive access point may be replaced by a new ‘age’ of computationally intelligent, socially communicative devices. This new approach can help researchers and technologists conceptualize transitions between usage of incumbent and emerging technologies.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".