Six Ways to Measure Status and Expectations
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
Abstract Since the 1960s, a research tradition has developed that centers on studying structures and consequences of status differences in group interaction. Scholars from many countries, including Australia, Canada, France, Germany, Holland, Israel, Turkey, and the United States, have produced a substantial body of work developing and extending theories of status processes. Others draw on such theories to analyze natural settings and to intervene to produce desired outcomes in groups. Two theoretical concepts are key to this research tradition: status characteristics and expectation states. Both concepts need operational measures for empirical test and application. While researchers may employ ad hoc measures of status and expectations, comparability across studies and cumulative theoretical development both benefit from the use of shared or standard measurement operations. The authors review six alternate research designs for studying status and expectation states. They identify what is known about each, what remains to be determined, and how each design might be developed for greater usefulness in research conducted in this and related theoretical traditions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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