Bibliographic record
Abstract
To the Editor: The original Microaggressions Triangle Model developed by Ackerman-Barger and Jacobs positions the “recipient,” “source,” and “bystander” involved in an act of microaggression with a suggested response for each party, respectively, at the vertices of a triangle; the sides appear to represent the relationships between the 3 participants. 1 In their AM Last Page, Poorsattar and colleagues have modified the original model to locate the actors along the sides of a triangle. 2 This alteration, while subtle, suggests that the 3 parties are equally responsible and accountable to address the microaggression. However, responsibility should sit primarily with the “source,” and not be shared equally with the “recipient.” This newer model conveys the wrong message. It is concerning that the model by Poorsattar and colleagues has been disseminated in its current form, and as such, may be accepted into wider discourse over the original model by Ackerman-Barger and Jacobs. Ackerman-Barger and Jacobs focus on individual microaggressions; consequently, their model does not include institutions or organizations, and is not used to describe systemic, systematic, or structural racism, for which the term “macroaggression” is often used. In contrast, Poorsattar and colleagues do include the “institution” in their model, implying that their model is not limited to individual microaggressions. Unlike their recommendations for “recipients,” “sources,” and “bystanders,” there is no accompanying reference for the actions they suggest “institutions” perform. The lack of supporting evidence is problematic. Moreover, they have placed the “institution” next to the “bystander” along the same side, but institutions and organizations are not “bystanders.” They are the workplace contexts in which microaggressions play out. Workplace culture greatly influences the likelihood of microaggressions being inflicted; the harm done; and the probability of acknowledgment, apology, and reconciliation being undertaken. Poorsattar and colleagues’ model fails to represent organizations’ moral duty to establish and ensure safe workplaces. Although the original model by Ackerman-Barger and Jacobs is less problematic than the modified version created by Poorsattar and colleagues, both models require the use of language that more honestly and accurately reflects the experience of a microaggression. While using terms such as “perpetrator” carries heavy emotional meaning, the use of neutral words such as “source” does not justly reflect the targeting of the “recipient” and the resulting harm done to the one on the receiving end of a microaggression. Calling things by their proper names is the beginning of meaningful dialogue.
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 | 0.011 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.015 | 0.028 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".