Indie media and digital community collaborations in public libraries
Bibliographic record
Abstract
Purpose – This paper aims to examine the current state of collecting with emphasis on small, independent and local digital media for the purpose of exploring librarians’ tools to develop unique collections with these types of cultural products included. Design/methodology/approach – This conceptual paper is based on examination of the current state of publishing and digital media, of case profiles of independent digital content providers, of case profiles of public libraries using digital media to expand collections and of collection developers’ tools, including reviewing sources. Findings – With regard to expanding collections from small, independent and local digital content providers, user-generated content (UGC) is offered as a tool for collection developers to use alongside other traditional reviewing sources. UGC allows for embedding collective voices into collection development practices to capture digital cultural products from these providers. Originality/value – This paper reflects on the current state of digital content creation and publishing, including the limitations and possibilities in place for the future of public library collections from both large publishing companies and smaller media creators. Non-traditional digital media are cultural products produced for consumption and reception; therefore, we consider how these materials fit into contemporary collections, how they are connected to public libraries and subsequently are made available to library users.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.027 | 0.015 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".