Is It Content or Style? An Evaluation of Two Competitive Measurement Models Applied to a Balanced Set of Ethnocentrism Items
Bibliographic record
Abstract
Attitude surveys often use sets of items with identical response scales (e.g., in Likert format) in order to formulate attitude constructs. There is considerable evidence that such a response format can be susceptible to acquiescence bias (i.e., the tendency to agree with survey questions). The specification of a style factor (acquiescence) in measurement models can result in well-fitted factorial invariant models for cross-cultural surveys. Attempts to model acquiescence are confronted with the phenomenon that models with a positive and a negative factor appear to be as likely as models with a bipolar factor and a style factor. We will evaluate the two competitive measurement models, applied to a balanced set of ethnocentrism items from the 1999 Religious and Moral Pluralism (RAMP) dataset. It is argued that the competing measurement models should be evaluated in the context of a theoretically meaningful nomological network.
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 | 0.238 | 0.419 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| 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".