Digital Storytelling: An Opportunity for Libraries to Engage and Lead the Community
Bibliographic record
Abstract
This paper describes a case study investigation of the “Love Your City, Share Your Stories” digital storytelling initiative in Hamilton, Ontario. Data collection involved one-on-one interviews, document review, and participant observations with governance stakeholders from the Hamilton Public Library, McMaster University Library, and the City of Hamilton. Cet article décrit l'étude du cas de l’initiative numérique "Aimez votre ville, partagez vos histoires" à Hamilton, en Ontario. La collecte des données comprenait des entrevues en tête-à-tête, l'examen de documents, et l’observation participante auprès des membres de la direction de la Bibliothèque publique de Hamilton, de la bibliothèque universitaire de l’université McMaster, et de la ville de Hamilton.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".