Quantifying Turing: a systems approach to quantitatively assessing the degree of autonomy of any system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper describes a method by which the degree of autonomy of a system can be quantified in a manner that allows comparison between systems. The methodology revisits, refines, and extends the contextual autonomous capability (CAC) model proposed by the National Institute of Science and Technology (NIST) by defining three orthogonal system metrics against which the performance of a system may be assessed. During the development of this model, it was recognized that there existed two different but coupled domains of autonomy — the Executive Autonomy describing the degree of independence of a system during the execution of the mission; and the Developmental Autonomy describing the degree of independence of the system during preparation for the mission. The resulting methodology is explicitly developed to be system agnostic such that it could be applied to humans as well as computerized systems. As such, it provides a means of quantifiably comparing the performance of any two systems — including human and computer — that are performing comparable sets of missions. The proposed model is called the system-agnostic quantification of autonomy levels (SQuAL) model.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it