Bibliographic record
Abstract
The Public Libraries Act of Ontario, which applies to libraries \nin cities, towns and villages throughout the Province, provides the \ngeneral management, regulation and control of Public Libraries in \ncities through a Public Library Board, composed of the Mayor of the \nCity or a member of the City Council appointed by him as his representative, \nthree persons appointed by the City Council and three by \nthe Board of Education, each for a 3 -year period, and two by the \nSeparate School Board, each for a 2 -year period. \nThe dates of foundation for library boards in the Metropolitan \nToronto area are as follows: \n1883 Toronto 1945 York Township \n1914 Western 1950 East York Township \n1915 Mimico 1950 Etobicoke \n1921 New Toronto 1955 North York Township \n1922 Swansea 1955 Scarborough Township \n1944 Leaside 1955 Forest Hill \n1944 Long Branch \nThe Toronto Public Library Board serving 680,000 residents \nout of more than 1,700,000 in the Metropolitan area maintains, in \naddition to the Central Library, 21 branches throughout the city, \ntogether with public libraries in 6 hospitals and more than a dozen \nother public institutions. Other municipalities maintain a total of 26 \nlibraries, ranging in size from 3,000 to 170,000 volumes.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.774 | 0.488 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".