Volumes of evidence: examining technical change in the last century through a new lens
Bibliographic record
Abstract
Abstract New indicators of technical change based on titles included in the catalogue of the Library of Congress and on Amazon.com's website are presented along with evidence that they do capture technological innovation. The indicators are used to chart the pattern and nature of technical change over the last century. A strong, causal relationship is found to exist between these indicators and changes in TFP and output per capita. Moreover, innovations in some subgroups have had a greater impact on output and productivity than in others and the key players change over time. Information technologies are currently the dominant subgroup. JEL classificatiion: E32, O3, O4, N1 On présente de nouveaux indicateurs de changement technique basés sur les titres inclus dans le catalogue de la Bibliothèque du Congrès et sur le site de Amazon.com, ainsi que des résultats qui indiquent qu'ils saisissent l'innovation technologique. Les indicateurs sont utilisés pour cartographier le pattern et la nature du changement technique au cours du dernier siècle. On trouve qu'il existe une relation causale forte entre ces indicateurs et les changements dans la productivité totale des facteurs et le produit per capita. De plus, des innovations dans certains sous-groupes ont eu un impact plus grand que d'autres sur la production et la productivité, et les joueurs clés ont changé avec le temps. Les technologies de l'information sont pour le moment le sous-groupe dominant.
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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 | 0.020 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.094 | 0.131 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.024 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".