RSS (Really Simple Syndication): helping faculty and residents stay up todate
Bibliographic record
Abstract
Introduction It is imperative that clinicians and researchers in the health sciences stay current with advances in their fields. Many find themselves pressed for time and struggle with the volume of information available to them (Davies, 2007; Wallis, 2006). RSS (Really Simple Syndication) offers a relatively simple approach to managing the flow of new information that is published on the web. This chapter presents an overview of RSS and a case study of teaching RSS to health sciences faculty and medical residents. What is RSS? RSS is a web-based syndication format that allows information feeds from multiple websites to be aggregated into one place. Typically, the information from an RSS feed is sent directly to a user's desktop or web browser via a feed reader (also known as an aggregator), so that whenever a website is updated the user receives the new information automatically. From a technology perspective, RSS is an XML-based format which is fairly simple for information services and content providers to produce and for consumers to use. It is commonly used to subscribe to blogs and podcasts, as well as to generate dynamic web content (e.g. personalized home pages such as iGoogle). RSS originated in the late 1990s but did not become popular until the rise of Web 2.0 in the mid 2000s. RSS in library and information services Many librarians have identified RSS as a beneficial technology for current awareness. Librarians have traditionally been involved with the provision of current awareness services such as selective dissemination of information (SDI). These services have taken a variety of forms, from creating specialized publications that identify new literature to setting up electronic database alerts (Anderson, 1998; Shultz and De Groote, 2003). RSS feeds are perceived as being especially valuable for academics and professionals because they make use of a single interface (the feed reader) to manage information from new media such as websites and blogs, as well as from traditional information sources such as scholarly journals (Anderson, 2006; Cooke, 2006; Giustini, 2006). Much of the journal literature about RSS is purely descriptive, focusing on what the technology is and how it can be used in libraries or information services (Cohen, 2008; Holvoet, 2006; Tennant, 2003). In implementation, RSS has been used to promote new resources, deliver library news, and provide database alerts (Armstrong, 2007; Blansit, 2006; Corrado and Moulaison, 2006).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.104 | 0.084 |
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".