School libraries as power-houses of empathy: People for loan in The Human Library
Bibliographic record
Abstract
The capacity of literary fiction to foster the trait of empathy in readers has received significant recent coverage in popular and academic literature (see for example, Gavigan & Kurtts 2011). School libraries, through their natural connection to storytelling in all its forms, can help foster a culture of empathy and respect, and can play an active role in promoting tolerance within the school community. This paper will focus on the worldwide movement known as The Human Library, which aims to “challenge prejudice through conversation” (Human Library UK 2016a). The Human Library reimagines the concept of a reader opening a book and looking at life through the eyes of ‘the other’ as they turn the pages, by replacing physical books with ‘living books’: people from all walks of life who volunteer to be ‘borrowed’ by a ‘reader’ for a conversation. Through respectful dialogue, stereotypes can be broken down and empathy can replace stigmatization. This paper will focus on how any school can host a Human Library to build a culture that celebrates, rather than fears, diversity and difference.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.029 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.001 | 0.024 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".