The racialization of expertise and professional non-equivalence in the humanitarian workplace
Bibliographic record
Abstract
This paper aims to explore the ways which expertise is covertly racialized in the contemporary humanitarian aid sector. While there are considerable discussions on the expat-local divide among aid professionals, such dichotomization is still inherently nationality-based, which may be an over-simplified explanation of the group dimensions within aid organizations. This study seeks to uncover that professional categorizations of "expatriate" and "local" are not race-neutral and, instead, colorblind. Organizations within the contemporary humanitarian aid apparatus have come to appeal to what Michael Omi and Howard Winant would characterize as a new racial discourse-one that does not require explicit references to race in order to be perpetuated, as racial subordination has been reconfigured to rely on implicit references to race woven within the everyday social fabrics of the humanitarian profession. The research suggests that embedded under the contemporary professional structure of the liberal humanitarian space is a covert power hierarchy fueled by perceptions of expertise and competency along racial lines-particularly around one's whiteness.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".