Bibliographic record
Abstract
Academic Abstract Despite increased popular and academic interest, there is conceptual ambiguity about what allyship is and the forms it takes. Viewing allyship as a practice, we introduce the typology of allyship action which organizes the diversity of ways that advantaged individuals seek to support those who are disadvantaged. We characterize allyship actions as reactive (addressing bias when it occurs) and proactive (fostering positive outcomes such as feelings of inclusion, respect, and capacity), both of which can vary in level of analysis (i.e., targeting oneself, one or a few other individuals, or institutions). We use this framework to profile six productive yet largely independent bodies of social psychological literature on social action and directly compare relative benefits and constraints of different actions. We suggest several future directions for empirical research, using the typology of allyship to understand when, where, and how different forms of allyship might succeed. Public Abstract Despite increased popular and academic interest in the word, people differ in what they believe allyship is and the forms it takes. Viewing allyship as a practice, we introduce a new way (the typology of allyship action) to describe how advantaged individuals seek to support those who are disadvantaged. We characterize allyship actions as reactive (addressing bias when it occurs) and proactive (increasing positive outcomes such as feelings of inclusion, respect, and capacity), both of which can vary in level (i.e., targeting oneself, one or a few other individuals, or institutions). We use this framework to profile six large yet mostly separate areas of social psychological research on social action and directly compare the relative benefits and limitations of different actions. We suggest several future directions for how the typology of allyship action can help us understand when, where, and how different forms of allyship might succeed.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".