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Six Ways to Measure Status and Expectations

2022· book-chapter· en· W2891762200 on OpenAlexaboutno aff
Murray Webster, Joseph Dippong

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocial Power and Status Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsComparabilityTest (biology)Positive economicsEmpirical researchPolitical sciencePsychologyEconomicsStatisticsMathematics

Abstract

fetched live from OpenAlex

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.022
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.055
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.006
Science and technology studies0.0010.005
Scholarly communication0.0060.010
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.002

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.

Opus teacher head0.041
GPT teacher head0.296
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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