Assessment of Multilingual Collections in Public Libraries: A Case Study of the Toronto Public Library
Bibliographic record
Abstract
Abstract Objective – The Toronto Public Library has been frequently identified as having an exemplary multilingual collection to serve the information needs of the most diverse population in Canada; however, there is no evidence or collection assessment information available in the literature to validate those claims. This research sought to gain an understanding of the current state of their multilingual collection and compare it to the most recent multicultural population demographics. Methods – This was a case study of the Toronto Public Library multilingual collection using data collected from their online public access catalogue in November 2017. Data was collected about all languages available, with English, French, and the 17 most spoken mother tongues explored in more detail. Language results from the Statistics Canada 2016 Census of Population were also collected. Data was used to calculate and compare the English, French, and language collections to the population of reported mother tongues spoken in Toronto. Results – It was found that the Toronto Public Library has items in 307 languages. While the collection comprises many languages, there is far more focus on official language items than any other language compared to the population in terms of number of items and variety of formats. All 17 non-official languages that were studied had fewer items proportionally available in the catalogue than the proportion of speakers with that mother tongue. Conclusion – The high circulation rates of the Toronto Public Library’s multilingual collection indicate that it has had some success in meeting the needs of its community. However, as the largest library system in Canada with a highly regarded multilingual collection and with many resources for collection development, the Toronto Public Library falls short of having a language collection that is proportional to the languages spoken within the community. While it may not be possible to have a multilingual collection that is entirely representative of the community, this study shows that libraries can use census data to monitor population shifts in order to be responsive to the information needs of their changing communities.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".