Bibliographic record
Abstract
Steve Jobs described computers as "bicycles for the mind," a tool that allowed people to dramatically leverage their capabilities.This paper presents a formal model of cognitive tools and technologies that enhance mental capabilities.We consider agents engaged in iterative task improvement, where cognitive tools are assumed to be substitutes for implementation skills and may or may not be complements to judgment, depending on their type.The ability to recognise opportunities to start or improve a process, which we term opportunity judgment, is shown to always complement cognitive tools.The ability to know which action to take in a given state, which we term payoff judgment, is not necessarily a complement to cognitive tools.Using these concepts, we can synthesise the empirical literature on the impact of computers and artificial intelligence (AI) on productivity and inequality.Specifically, while both computers and AI appear to increase productivity, computers have also contributed to increased inequality.Empirical work on the impact of AI on inequality has shown both increases and decreases, depending on the context.We also apply the model to understand how cognitive tools might influence incentives to automate processes and allocate decision-making authority within teams.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".