Bibliographic record
Abstract
Abstract The article argues that academics navigate and occupy various localities, spaces, and identities, which allows them to be self-reflexive in understanding the inherent challenges in diversifying the discipline. Using personal narratives as a methodological and theoretical tool, this article situates plural experiences and contexts of a woman of color, working in precarity in academia. The intersection of multiple identities reveals various sites of privilege and oppression, and inclusion and exclusion. Unsettling and dismantling binaries and identities reveal complex entanglements and connections that provide more nuanced understandings of IR. This article further discusses ways the discipline of IR has excluded diverse theoretical and empirical knowledges and regions, including critical approaches and the Global South. This disciplinary exclusion and erasure is reproduced in everyday academic practice and can serve as an entry point to understand why diverse communities are underrepresented in IR. Further, academia is not immune from the functions of power and social and economic hierarchies in society, and those hierarchies are manifested in various forms of asymmetry observable in academia, especially toward diverse communities and academics working in precarity.
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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".