Breaching the walls of academe: the case of five Afro-Caribbean immigrant women within United States institutions of higher education
Bibliographic record
Abstract
While a growing tendency among researchers has been for the examination of diverse forms of discrimination against Afro-Caribbean immigrants within the United States (US), the types of ambiguities that these create for framing the personal and professional identities of Afro-Caribbean women academics who operate within that space remain relatively absent. The literature is also devoid of substantive explorations that delve into the ways and extent to which the cultural scripts of Afro-Caribbean women both constrain and enable their professional success in academe. The call therefore is for critical examinations that deepen, while extending existing examinations of the lived realities for Afro-Caribbean immigrants within the US, and, the specific trepidations that they both confront and overcome in the quest for academic success while in their host societies. Using intersectionality as the overarching framework for this work, we demonstrate, through the use of narrative inquiry, the extent to which cultural constructions of difference nuance the social axes of power, the politics of space and identity, and professional outcomes of Afro-Caribbean immigrant women who operate within a given context. These are captured within our interrogation of the structures of power that they confront and their use of culture to fight against and to break through institutional politics.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.056 | 0.016 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".