Crowding the library: How and why libraries are using crowdsourcing to engage the public
Bibliographic record
Abstract
Over the past 10 years, there has been a noticeable increase of crowdsourcing projects in cultural heritage institutions, where digital technologies are being used to open up their collections and encourage the public to engage with them in a very direct way. Libraries, archives and museums have long had a history and mandate of outreach and public engagement but crowdsourcing marks a move towards a more participatory and inclusive model of engagement. If a library wants to start a crowdsourcing project, what do they need to know? This article is written from a Canadian University library perspective with the goal to help the reader engage with the current crowdsourcing landscape. This article’s contribution includes a literature review and a survey of popular projects and platforms; followed by a case study of a crowdsourcing pilot completed at the McGill Library. The article pulls these two threads of theory and practice together—with a discussion of some of the best practices learned through the literature and real-life experience, giving the reader practical tools to help a library evaluate if crowdsourcing is right for them, and how to get a desired project off the ground.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.037 | 0.037 |
| Scholarly communication | 0.043 | 0.030 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".