Bibliographic record
Abstract
This chapter outlines the experience of a multiracial, early-career academic librarian in Canada, whose first job out of school included supervising six support staff. Using counterstorytelling, a method rooted in critical race theory, this chapter discusses the challenge of bringing one's authentic self to supervisory roles in a white-dominant space, the particular microaggressions that afflict IBPOC middle managers from both upper management and direct reports, and the importance of seeking support from both IBPOC and non-IBPOC allies. The chapter begins with a basic introduction to critical race theory and the counterstorytelling method, followed by an overview of the literature on the racial climate in Canadian libraries, particularly in management roles. The author then goes on to share a series of counterstories, which are then analyzed from a critical race theory perspective. Finally, the author offers recommendations to fellow early-career IBPOC librarians who might find themselves in a similar predicament.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.031 | 0.008 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".