Bibliographic record
Abstract
“I read Little House on the Prairie when I was a child and it didn’t make me racist”,“the kids love Indian in the Cupboard”, “it is our history we should include it and not censor”.These are all sentiments expressed in librarian groups. How much of this sentiment is based on ourown nostalgia for the books of our youth? If we were to engage in rereading these books that are ofthe childhood canon would we be so certain that we should engage our youth in experiencing thesetitles? Would we be defensive of arguments that these books need to be carefully curated andintroduced to students? Or not introduced at all? Censorship and the recent attempts in the UnitedStates as a backlash to diversity, equity and inclusion practices tend toward simple solutions ofcomplex conversations. Rereading childhood favorites may complicate the answers and bringnuance to a complex conversation. This paper examines one person's attempt to reckon with whatthe books of her youth taught her, while also opening discussion with practitioners about the impactof rereading on their own practice.
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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 | 0.037 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.049 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".