PressReader - The changing landscape to digital transcript
Bibliographic record
Abstract
PressReader -The changing landscape to digitalLisa Hewitt: This webinar is being recorded, and will be shared with attendees within the next week.The video of this will also be available on our events web page after the event.So, you can point members of your institutions to watch it later.All attendees are on mute at the moment, that's just a standard for our Webinars.We will welcome any questions within the chat pane or in the Q. & A. pane at any time, and these might be answered, as we move along, or they will be answered in our Q.& A. session at the end of this webinar.The agenda is as follows: there is an introduction and demonstration.Fireside chat, and then the question-and-answer session.So, I would like to hand over now to our colleagues at PressReader.Oghenevwoke Shehu Usman: Thank you very much, Lisa.Thank you for, for putting this together, and very warm welcome to all attendees of the of this of this Webinar.My name is Oghenevwoke, and I work for PressReader.I thank you all for joining this this webinar.So without further ado, I'm just going to go ahead and share my screen.Okay, as I said my name Oghenevwoke, I, I work for PressReader, we're media Technology Company, we're based out of Vancouver, Canada.Today we will be discussing a very, very important topic which is about the we will be discussing the 20 first century Liberian and the changing landscape to digital.We have a very, very important guest today on, on, on, on this call, and who'll be sharing some insights about how the University of Bath have been navigating the, the changing landscape to digital.As we all know, most industries around the world have been disrupted by technology, and of course the library industry is not, it is not without, you know it is not left out of the of the disrupt, of the disruption.So, I'm just going to go ahead, and, you know, Start the conversation just a little bit about PressReader, and then also look at some of the digital trends that we see going on around the world.And then after that we would go into a fireside chat with Katrin Roberts, to tell us a bit about how they're making things work in University of Bath.So this is basically we'll just talk about, you know as I said, there's the introduction.What about, talk about PressReader, digital trends and what it means for the library industry.And then we can move onto Katrin.Okay, so PressReader, we are a technology company, as I said before, we're based out of Vancouver, Canada.We curate newspapers and magazines from around the world, and we make them available to libraries around the world, to academic and public.So 7,500 plus newspapers and magazines, newspapers available to your readers.Content from over 120 different countries, and then 60 plus different languages.And we make all that available to you in your libraries.And some powerful features are the content both local and international, so content from around the world.Just add, any other Academic institution in the world, students come from all over, so it makes sense.So it makes sense to have a, a resource that provides content from around the world.Content on there is current day.So you have the latest editions and then you also have an archive of up to 20 years ago, which allows your students, you know, to improve the quality of their work when they turn in their essays.And we also have a personalisation feature that allows personal delivery of content.So our, our algorithm is actually study user behaviour and make content suggestions based on their, on their preferences, and also the keyword search, which is very, which is a very important tool for students when they do their research, so they can actually search, on or all based on topics that they're interested in on their just to improve the quality of their essays.Also download features also available for students and faculty members.So, students can actually download content in the Wi-fi zone and then still have access to the content when they are not in your wi-fi zone.There's also the share feature which allows students, encourage collaboration, you know, between peers and also between faculty members and students.And there's also the translation feature.that allows content to be translated in different languages and very good for students, learning, languages in universities.There is also the listening feature, which is very good for accessibility for people who are visually impaired.Now, as we just before we go into the main into some of the digital trends that we see.I, I came across this very interesting quote, and I thought to share with the rest of the group, and says: "Libraries of the future will not be defined by how fast they adopt digital, but by how did they transform their digital services to meet the rapidly changing audience expectations".
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.014 |
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 teacher head, 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".