Just Curious: How Can Academic Libraries Incite Curiosity to Promote Science Literacy?
Bibliographic record
Abstract
Based on a Bright Young Minds webinar given on February 7, 2017, this paper shows the importance of nurturing curiosity in students as an integral part of information literacy (IL) and science literacy. There are obvious parallels between "Research as Inquiry," as described in the ACRL Framework for Information Literacy for Higher Education (2016) and scientific inquiry. In both cases, curiosity is the fuel that drives information gathering and the pursuit of new knowledge. This paper discusses three pedagogical strategies to help information literacy librarians incite curiosity in students and promote scientific literacy. Bright Young Minds is a webinar series hosted by the Ontario Library Association’s Education Institute. It provides a platform for MLIS students and recent graduates to share their research and to foster connections between academic schools and information professionals. The webinar and this subsequent article grew out of an MLIS project exploring the concept of curiosity and its application in promoting scientific literacy in academic libraries. I draw on my dual experiences as both a Chemistry graduate student and participant in IL sessions, and as a recent MLIS graduate and IL instructor.
 Basé sur le webinaire « Bright Young Minds » offert le 7 février 2017, cet article montre l’importance de favoriser la curiosité auprès des étudiants comme élément essentiel de la formation documentaire et de l’enseignement des sciences. Il existe des parallèles évidents entre la notion de « recherche comme enquête », telle que décrite dans le « Framework for Information Literacy for Higher Education (2016), » et la recherche scientifique. Dans les deux cas, la curiosité mène la recherche documentaire et la poursuite de nouvelles connaissances. Cet article présente trois stratégies pédagogiques pour aider les bibliothécaires de formation documentaire à inciter la curiosité auprès des étudiants ainsi que promouvoir la recherche scientifique. « Bright Young Minds » est une série de webinaires offert par l’Education Institue de la Ontario Library Association. Elle offre une occasion aux étudiants en science de l’information et aux nouveaux diplômés de partager leur recherche ainsi que de tisser des liens entre les écoles de bibliothéconomie et les professionnels de l’information. Le webinaire et cet article subséquent émergent d’un projet d’école explorant le concept de la curiosité et de son utilisation dans le contexte de la promotion de la recherche scientifique au sein des bibliothèques universitaires. Mes propos sont basés sur mes expériences en tant qu’étudiant diplômé en chimie et participant à des sessions de formation documentaire et en tant que récent diplômé en sciences de l’information et formateur en recherche documentaire.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.028 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".