The Epistemology of Measurement: A Model-based Account
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
Measurement is an indispensable part of physical science as well as of commerce, industry, and daily life. Measuring activities appear unproblematic when performed with familiar instruments such as thermometers and clocks, but a closer examination reveals a host of epistemological questions, including: 1. How is it possible to tell whether an instrument measures the quantity it is intended to? 2. What do claims to measurement accuracy amount to, and how might such claims be justified? 3. When is disagreement among instruments a sign of error, and when does it imply that instruments measure different quantities? Currently, these questions are almost completely ignored by philosophers of science, who view them as methodological concerns to be settled by scientists. This dissertation shows that these questions are not only philosophically worthy, but that their exploration has the potential to challenge fundamental assumptions in philosophy of science, including the distinction between measurement and prediction.
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 | 0.005 | 0.004 |
| 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 | 0.002 | 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