Bibliographic record
Abstract
This article emerges from a sustained transnational dialogue between two cisgender female, first-generation immigrant scholars of Japanese origin – one identifying as Nikkei in Sweden and the other as mixed-race Japanese (white) in Canada. Through collective autobiographical inquiry, we explore what we term a middle space – a site of ambiguity, tension, and transformation where intersectionality is both theorized and embodied. We situate our personal narratives within broader structural frameworks to contribute to scholarship that treats intersectionality as both theory and praxis – a tool for critical reflection and social transformation. Guided by critical feminist and collective methodologies, our writing emphasizes reflexivity, dialogue, and the interrogation of power dynamics in knowledge production. Our lived experiences reveal how positionalities shaped by sociocultural and institutional contexts resist binary categorizations of privilege and marginalization. These identities are continuously negotiated and inform our academic and personal engagements. We underscore the importance of collective methodologies in illuminating complex positionalities and advancing intersectional feminist scholarship. By inviting readers into our middle space, we offer a site of intersectional engagement, activism, and reflexivity where theory meets lived experience, and where shifting dynamics of power and identity are critically examined.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.102 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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.
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".