Friendship as a lifeline: Navigating the precarious landscape of the US employment‐based immigration as women of color psychologists
Bibliographic record
Abstract
Drawing inspiration from the reflective and liberating practice of testimonio, we narrated our experiences as two women of color precariously employed psychologists with US employment visas. Shirley Ley recounted an experience of lost identity as a psychologist at a small liberal arts institution while Shaznin Daruwalla wove a narrative of tested endurance in her role as a staff psychologist in a medium-sized state-funded academic institution. Despite our diverse origins-Canada and India respectively-we shared the intricate link between our immigration status and employment. This connection tethered us to our professional roles and the organizations supporting our employment-based immigrant visas. Unlike US permanent residents or citizens, our difficulty in switching employers freely left us profoundly vulnerable. The gravity of employment termination was overwhelming, tantamount to relinquishing our rights to reside and work in the United States, affording us only a brief 60-day window to secure new sponsorship. Guided by our unwavering anti-oppressive ethos, we confronted institutional barriers head-on, a stance that often placed us in precarious situations. Our assertive challenges to the system not only risked termination of our jobs but also carried the threat of displacement, a persistent reality in our consciousness. Amid the backdrop of COVID-19's employment uncertainties, our friendship emerged as a steadfast anchor, offering the safety and stability we needed to persevere. Through this paper, we sought to demonstrate how supportive relationships and shared experiences among women of color can be a powerful tool for resilience, personal growth, and professional empowerment in the face of systemic challenges.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.046 | 0.031 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.009 |
| 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".