Little Free Libraries®: Interrogating the impact of the branded book exchange
Bibliographic record
Abstract
In this article, we critique the phenomenon of Little Free Libraries® (LFL®), the non-profit organization dedicated to sharing books with one’s neighbours. Through our engagement with the discourses, narratives and geographies of the LFL® movement, we argue that the organization represents the corporatization of literary philanthropy, and is an active participant in the civic crowdfunding activities of the non-profit industrial complex. The visible positioning of these book exchanges, particularly on private property in gentrified urban landscapes, offers a materialization of these neoliberal politics at street level. Drawing primarily upon one of the author’s experiences as an LFL® steward, as well as critical discourse and GIS analysis, we offer constructive critiques of the organization and their mission, and suggest that the principles of community-led library practice can be more effectively employed to harness the enthusiasm of these self-described “literacy warriors.”
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.042 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".