Collection Development in Practice: Rural Public Library
Bibliographic record
Abstract
Library collection management is an ongoing process of selecting and deselecting – or weeding – materials based on institutional policies, budgets, and community needs. In this hypothetical collection management exercise, we were given a selection budget of $1000 and expected to examine and manage a portion of a rural public library’s collection. Our group chose the Brighton Public Library’s (BPL’s) print collection on Canada’s involvement in World War II. We compared publicly available community demographic and library use statistics for Brighton and Brighton Public Library with those of three other Ontario communities/libraries to complete a community assessment, and we compared BPL’s mission, vision, strategic plan, and collection management policy to its online public access catalogue to assess the current collection, and ultimately determine selection and weeding criteria. We also employed list-checking to select materials and, as per BPL’s policy, the Texas CREW Method to weed materials. Conducting this collection management exercise was much more difficult than expected; we struggled to spend the budget, selecting over 80 children’s and young adult titles based on the collection gap we identified for these age groups. At the same time, we weeded only three copies of books in the collection. We learned that successful collection management is not only rooted in library policy, library values, and community values/needs/wants; it is an iterative, reciprocal process.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".