How Does Measuring Generate Evidence? The Problem of Observational Grounding
Bibliographic record
Abstract
The epistemology of measurement is an area of philosophy that studies the relationships between measurement and knowledge. One of its central aims is to explain how measurement can function as a reliable source of scientific evidence. Key to such explanation is a clear characterization of the dependence of measurement on observation, but such characterization has remained elusive. This article traces the recent historical trajectory of views on the observational grounding of measurement, clarifies the current state of the problem, and proposes new directions for progress. Specifically, I argue in favour of viewing measurement outcomes as the best predictors of observed instrument indications under a given theoretical-statistical model of the measurement process. The evidential efficacy of measurement outcomes is explained by their relatively high epistemic security, rather than by their inferential or structural closeness to observation.
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.
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 | 0.153 | 0.453 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.057 |
| Scholarly communication | 0.011 | 0.036 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".