All Change! The Implications of Non-Stationarity for Empirical Modelling, Forecasting and Policy
Bibliographic record
Abstract
In an age of congested transport systems, everyone knows what it is like to be stationary: stuck motionless in a traffic jam; a train standing still at a station long after the due departure time; an aircraft sitting at the departure gate several hours delayed. The same word is used in a more technical sense in statistics: a stationary process is one where its mean and variance are constant over time.1 As a corollary, a non-stationary process is one where the distribution of a variable does not stay the same at different points in time– the mean and/or variance may change for many reasons. Non-stationarity is like a statistical version of the changeover point in a relay race — as they all change, one team successfully transfers, while another drops the baton, and a third is reaching towards a future transfer with an unknown outcome.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".