Bibliographic record
Abstract
To motivate the elaborate machinery of measure theory, it is desirable to show that in some natural space Q one cannot define a measure on all subsets of £2, if the measure is to satisfy certain natural properties. The usual example is given by the Vitali set, obtained by choosing one representative from each equivalence class of R induced by the relation x ~ y if and only ifx-yeQ. The resulting set is not measurable with respect to any translation-invariant measure on R that gives nonzero, finite measure to the unit interval [8]. In particular, the resulting set is not Lebesgue measurable. The construction above uses the axiom of choice. Indeed, the Solovay theorem [7] states that in the absence of the axiom of choice, there is a model of Zermelo-Frankel set theory where all the subsets of R are Lebesgue measurable. In this note we give a variant proof of the existence of a nonmeasurable set (in a slightly different space). We will use the axiom of choice in the guise of the wellordering principle (see the later discussion for more information). Other examples of nonmeasurable sets may be found for example in [1] and [5, Ch. 5]. We will produce a nonmeasurable set in the space Q := {0, 1}Z. Translationinvariance plays a key role in the Vitali proof. Here shift-invariance will play a similar role. The shift T : Z -> Z on integers is defined via Tx := x + 1, and the shift r : Q -> Q on elements co e Q is defined via (xco)(x) := co(x 1). We write xA := {xco : co e A] for A c Q.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".