Information Practices of Young People at a Public Library Makerspace – A Sense-Making Approach
Bibliographic record
Abstract
The purpose of this research was to understand how young people interact with information at a public library makerspace as well as the opportunities and challenges emerged in their participation. Dervin’s Sense-Making theory and methodology were employed in framing the research questions, data collection and analysis. Findings highlighted the informal learning opportunities of a makerspace, challenges that occurred during the making phase, information and help seeking from iterative trial and error as well as interpersonal resources. Implications for information professionals at public library makerspaces are discussed. Le but de cette recherche était de comprendre comment les jeunes interagissent avec l’information dans une bibliothèque publique équipée d’un atelier partagé, ainsi que les opportunités et les défis ayant émergé au cours de leur participation. La théorie et la méthodologie du sense-making de Dervin a été utilisée dans la formulation des questions de recherche, la collecte et l'analyse des données. Les résultats ont mis en évidence les opportunités d’apprentissage informel de l’atelier partagé; les défis ayant surgi lors de l’utilisation de l’atelier; les recherches d’information et d’aide apparues lors des phases d’essais et erreurs itératifs ainsi que des ressources interpersonnelles. Nous discutons les implications des ateliers partagés en bibliotjèques publiques pour les professionnels de l'information.
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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".