Academic Uses of Google Earth and Google Maps in a Library Setting
Bibliographic record
Abstract
Over the last several years, Google Earth and Google Maps have become increasingly utilized in academic libraries for promoting and delivering a variety of library services. These have included instructional collaborations with faculty to promoting student engagement across programs and campuses. Seeking to find out exactly how academic libraries were using Google Maps and Google Earth (if at all), the authors launched a online survey in December 2010 to see just what kind of impact the Google mapping products were having in the academic library setting. Receiving over 80 responses from academic librarians and staff from across Canada and the United States, the results showed that over 90% of the respondents use Google Earth and Google Maps for some area of their professional duties in a academic library. These included answering research questions, developing instructional materials for courses, and building tools to promotion and marketing. This presentation will discuss the survey results and summarize the way academic libraries are utilizing Google Earth and Google Maps for instruction, research, and student engagement.Depuis les dernières années, Google Earth et Google Maps sont de plus en plus utilisés en bibliothèque universitaire pour promouvoir et offrir une variété de services en bibliothèque, y compris l’enseignement collaboratif avec les professeurs afin d’accroître l’engagement étudiant, de tous les programmes et campus. Afin de déterminer exactement la mesure dans laquelle les bibliothèques universitaires utilisent Google Maps et Google Earth (le cas échéant), les auteurs ont lancé un sondage en ligne en décembre 2010 pour évaluer l’impact des produits de cartographie de Google dans les bibliothèques universitaires. Plus de 80 sondages ont rempli par des bibliothécaires et du personnel en bibliothèque d’universités au Canada et aux États-Unis. Les résultats démontrent que plus de 90 % des répondants utilisent Google Earth et Google Maps pour certaines de leurs activités professionnelles, y compris répondre à des questions de recherche, développer du matériel pédagogique et élaborer des outils de promotion et de marketing. Cette communication abordera les résultats de la recherche et résumera les modes d’utilisation de Google Earth et de Google Maps dans l’enseignement, la recherche et l’engagement étudiant.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.026 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".