Bibliographic record
Abstract
This paper presents a critique of the General Purpose Technology (GPT) framework. It argues that the GPT framework is fundamentally flawed as an approach to understanding growth as it focuses on what are input production technologies (hereafter input technologies), and not on the associated primary inputs and resulting outputs. Computers don't produce output; rather, they produce information which is necessary for the production of output. Dynamos don't produce output, rather they transform prime movers (steam, hydraulics, fossil fuels) into electricity which is transmitted to machines that ultimately produce output. Steam engines don't produce output; rather, they too transmit prime movers (fossil fuels) into output. The problem with the GPT framewoek lies with the implicit assumption that there exists a one-to-one relationship between the intermediate input and the primary input. We will show that this assumption was violated in all three classic GPT cases, which explains what Paul Davd referred to as the "electricity paradox" and what Robert Solow referred to as the "information paradox." We then proceed to present an alternative framework based on the concept of enabling 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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| 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".