If You See Something, Say Something : an anti-oppression framework for recognizing and responding to microaggressions in our libraries
Bibliographic record
Abstract
“Well, they ended up hiring someone who was in one of those diversity residency programs, so it’s no wonder I didn’t get an interview.”\n“It’s not like we really NEED a gender neutral bathroom in the library.”\n“Can you do the session for my class in the library lab? I know there aren’t enough computers to go around, but the students all have their own laptops, anyway, so they can just bring those.”\nAs librarians, we have a responsibility to take care of ourselves, our colleagues, and our patrons by ensuring that the libraries we work in are safe spaces.\nStatements like the ones above are examples of microaggressions, defined as “verbal, behavioral, and environmental indignities, whether intentional or unintentional, that communicate … slights and insults to the target person or group (Sue et al 2007).” These often thoughtless statements, whether they come from colleagues or patrons, can insidiously turn our libraries into unsafe spaces.\nCreating and maintaining a safe and welcoming environment in our libraries requires an anti-oppression mindset, motivation to act, and the skillset to address intolerance at all levels, from hate speech to unchallenged microaggressions.\nRepurposing New York City’s If You See Something, Say Something slogan provides us with a framework to identify and address microaggressions. In this brief presentation, we will introduce strategies for recognizing and responding to microaggressions when working with students, faculty, community members, or coworkers.\nSue, D., Capodilupo, C. M., Torino, G. C., Bucceri, J. M., Holder, A. B., Nadal, K. L., & Esquilin, M. (2007). Racial microaggressions in everyday life: Implications for clinical practice. American Psychologist, 62(4), 271-286. doi:10.1037/0003-066X.62.4.271
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.023 | 0.071 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".