Bibliographic record
Abstract
Social scientists of virtually all persuasions have to grapple in some way with the issue of identity. When it comes to understanding phenomena related to the collective, whether in the form of ethnicity, race, religion or the nation, there is a strong sense that identity matters. Indeed, a great many social scientists would argue that identification with a group can shape individual behavior, and that the notion of a group identity gives important meaning to collective action. The difficulty, however, lies in translating the theoretical importance of identity into the concreteness of the research process. In other words, working with the concept of identity brings significant methodological challenges. This, in turn, can lead researchers to avoid taking identity into account or simply considering it as context rather than placing it within a causal explanation. Measuring identity will help researchers make the most of this important social and political reality. This book examines various methodologies designed to incorporate identity into social science research. Together, the 12 chapters provide insight and guidance on how to approach identity and how to use it as an independent variable when conducting research on comparative politics, American politics and international relations. The first section of the book focuses on issues of definition and conceptualization. It contains three strong chapters that set the table for the subsequent discussions of the specific methodologies that can be used when working with identities. Of particular value is the first chapter “Identity as variable,” written by the editors, which defines “a collective identity as a social category that varies along two dimensions-content and contestation” (p. 19). The chapter then specifies the forms that can be taken by the content of an identity and discusses contestation in terms of degree.
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 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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".