Bibliographic record
Abstract
Abstract This chapter discusses the two major theories underlying scale development: classical test theory, which has dominated the field for the past century, and item response theory, which is more recent. It begins by summarizing the history of measurement, first of physical and physiological parameters and later of intelligence. This is followed by the steps involved in developing a scale: creating the items, determining if they fully span the construct of interest while at the same time not including irrelevant content, and assessing the usability of the items (whether they are understood correctly, whether they are free of jargon, if they avoid negatively worded phrases, etc.). The chapter then describes how to establish the reliability and validity of the scale—what are called the psychometric properties of the scale. It concludes by discussing some of the shortcomings with classical test theory, how item response theory attempts to address them, and the degree to which it has been successful in this regard. This chapter should be useful for those who need to evaluate existing scales as well as for those wanting to develop new scales.
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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 | 0.027 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.019 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".