Bibliographic record
Abstract
This is a vulnerable and brave opinion piece motivated by the hope for change and desire to inspire and open new opportunities for readers to become accomplices in racial justice movements.Through an intersectional theoretical perspective, a Black cisfemale dietitian shares her stories of distressing experiences of anti-Black racism within the feminist environment of dietetics.Using examples from the academic, practicum, and workplace settings along with comparisons to notable historical and contemporary incidences, the author highlights how a particular form of microaggression and entitlement, captured through the term "Karen", creates significant forms of oppression for students, dietitians, and patients of colour, particularly those living in Black skin.Furthermore, the selected narratives will demonstrate how the term calls attention to particular chosen behaviours and is not a slight.These "counterstories" to the dominant narrative are grounded in lived experiences and will be used to enlighten and prompt self-reflection.The intention is to give voice to people of colour who are struggling with the consequences of abused white female power without putting them at risk of penalization and judgment.The author will also offer honest recommendations that may be considered unconventional and bold within the profession, as initial steps towards allyship and facilitating anti-oppression.This will also allow space for white women working in the profession to question and consider how their perception of equity might be expanded to allow room for acceptance and diversity.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".