Bibliographic record
Abstract
The importance of technological innovation in defining and shaping our global economy has made it a central research topic over the past decade. The rise of electronics manufacturing technology, specifically the silicon transistor technology, is considered a major factor influencing technological innovation and in turn, affecting the world's economic and social transformation. The process of technological innovation generally involves getting new ideas accepted and converted into new technologies that are adopted and used. Sociologically, the innovation process can be observed as sequence of interconnected activities and mediations between human subjects and non-humans objects that are socially distributed and technologically connected. This paper observes technology industry's most eminent innovation edict known as Moore's Law, through one of sociology's most controversial theories, the actor-network theory (ANT). Suggested as a self-fulfilling prophecy resulting in a multibillion-dollar global technology industry and accredited to having put silicon in Silicon Valley, Moore's Law is often described as the driver for the information and communication technology revolution. Originally a prediction towards smaller, cheaper and more reliable computer processing power, this paper examines Moore's Law as a socio-technical innovation process. It proposes that Moore's Law is a complex assemblage comprised of interrelationships between ambitious scientists, chemicals, engineered technologies, culture and society. ANT is used as the theoretical framework to observe the progressive social relationships that constitute Moore's Law and introduce a translation. The objective of this experimental study is to examine the temporal socio-technical transformations and propose an alternative description for Moore's Law.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".