Use of Hashtags by Two Canadian Public Libraries: A Comparative Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research presents a comparative review of hashtags in tweets posted by the two large Canadian public libraries, Edmonton Public Library (EPL) and Calgary Public Library (CPL), serving communities in the Province of Alberta. The descriptive statistics reveals variation between the two libraries in the number and types of hashtags. Both the libraries used a number of hashtags that had libraries’ names including initialism to contribute to their visibility, and local airport codes or the respective city names to establish their explicit and implicit associations with their geographical area of operations. The paper contributes to literature on the use of hashtags particularly in the context of Canadian public libraries. It will provide evidence-driven insights to other libraries on ways to create hashtags to strengthen their online presence, and digitally share information and promote events, programs and services.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.008 | 0.029 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it