Overcoming the Invisibility of Metrology: A Reading Measurement Network for Education and the Social Sciences
Bibliographic record
Abstract
The public and researchers in psychology and the social sciences are largely unaware of the huge resources invested in metrology and standards in science and commerce, for understandable reasons, but with unfortunate consequences. Measurement quality varies widely in fields lacking uniform standards, making it impossible to coordinate local behaviours and decisions in tune with individually observed instrument readings. However, recent developments in reading measurement have effectively instituted metrological traceability methods within elementary and secondary English and Spanish language reading education in the U.S., Canada, Mexico, and Australia. Given established patterns in the history of science, it may be reasonable to expect that widespread routine reproduction of controlled effects expressed in uniform units in the social sciences may lead to significant developments in theory and practice.
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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 | 0.030 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".