Bibliographic record
Abstract
School libraries in Canada and the United States have a very short history compared to other types of libraries in North America; however, their histories have many similarities and some differences.While school libraries in both countries developed within their educational systems, Canada launched a more nationalized approach for its provinces, and the U.S. systems grew state by state. IN THE BEGINNINGCanadian education was modeled first after the French and then Great Britain, controlled by the church.Later the Ontario province passed legislation creating a school system with municipally elected boards, local taxation, and a central administration of education which was copied by the Constitution of 1867 giving control of education to the provinces.Designed by a Methodist minister, Egerton Ryerson, they copied the schools of the New England colonies.School libraries were often referred to as important in achieving the goals of education.(1) In the U.S., all government not covered by the Constitution is assigned to the individual states making education a part of state governance.The first schools in the United States were very small and in the hands of the headmaster.Students (usually male students) were asked to copy information from the board to their slates.They might learn how to read and write, but this came independently from what they were copying.Schooling was labor-intensive and available only to those who could pay.As the country grew, those in charge wanted a citizenry able to read and write, live and vote in a democratic society.In some of the larger cities, societies were formed to provide free education to the masses so they would become responsible citizens.These nineteenth-century societies adopted a method in which many students were organized into groups based on ability.The older students learned from the master, and then taught the younger ones.They copied and were drilled on facts until they knew them.(2) Slowly, but steadily, state governments across the nation built public schools, teachers were hired to educate all children, and school attendance became compulsory.Educational funding in the U.S. is left to state laws, and this varies by an individual state's ability and willingness to support education.Problems in equity arise.A current situation has to do with being able to find qualified teachers.In 1996, the National Commission on Teaching and America's future stated:Although no state will allow a person to fix plumbing, guard swimming pools, style hair, write wills, design a building, or practice medicine without
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.132 | 0.078 |
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".