Government documents reference service in Canada: A nationwide unobtrusive study of public and academic depository libraries
Bibliographic record
Abstract
This study reports on a nationwide unobtrusive evaluation of govern-ment documents reference service at public and academic depository li-braries in Canada. Fifteen questions dealing with subject matter from both the legislative and executive branches of government were asked 488 times at 104 depository libraries in 30 census metropolitan areas. Overall, depository library staff members provided complete answers to questions 29.3 % of the time. When complete and partially complete answers are counted together, the success rate climbs to 42.4%. Aca-demic full depositories achieved the highest rate of success, followed by public full depositories. In-person questions were answered more suc-cessfully than phone questions. Print materials were by far the largest single source used (45.7%) to answer questions. When print alone was used, complete answers to the test questions were found only 39.9 % of the time. When World Wide Web sources alone were used, the com-plete answer rate was 60.7%. To improve service, extensive and peri-odic staff training may be needed about the structures and functions of both the legislative and executive branches of government. Staff mem-bers need to know what programs are available and who is responsible for which program in the federal government. Unobtrusive evaluation studies concerning the efficacy of library reference ser-vice have consistently shown that librarians are able to offer complete and satis-factory answers to patrons about 55 % of the time (Hernon & McClure, 1986) and that five variables (library expenditures, volumes added, fluctuations in the collection, size of the service population, and hours of operation) “reveal a con-Direct all correspondence to:
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".