Bibliographic record
Abstract
Purpose Despite the hype about transformative technology, the authors of “Power and Prediction: The Disruptive Economics of Artificial Intelligence” see the recent AI advances as all basically ‘better statistical techniques’ that allow us to take really big data sets and come up with more refined predictions. Design/Methodology/Approach University of Toronto experts, Ajay Agrawal, Joshua Gans and Avi Goldfarb explain why transformation of business model by AI will be some time in the future when we move beyond simply substituting the new technology into existing systems and start to leverage its potential to enable the reimagining of old system solutions and innovate radically new value propositions. Findings What economic history tells us is that technology-driven transformation does not come easy and real adoption only occurs when new systems are created. Practical Implications As there are likely many decisions in your organization that have been codified into rules, AI offers the potential to turn them to dynamic decisions. Originality/Value To realize the full potential of AI, companies need to adopt a “system mind-set,” in contrast to the “task-level thinking” that still predominates in most companies.
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 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".