Portable science: podcasting as an outreach tool for a large academicscience and engineering library
Bibliographic record
Abstract
Background and introduction to podcasting In January and February 2005, the Pew Internet and American Life Project conducted a survey of iPod/MP3-player users and found that one in five ‘age 18 and older’ own an iPod or MP3 player (Pew Internet, 2006). More recently, eMarketer estimated that the total US podcast audience reached 18.5 million in 2007. That audience will increase by 251% to 65 million by 2012. Of those listeners, 25 million will be ‘active’ users who tune in to podcasts at least once a week (eMarketer, 2008). An Australian academic study that measured undergraduate use and ownership of emerging technologies found that in 2007 more than 70% of under - graduates owned iPod or MP3 players, up from 40% in 2005 (Oliver and Goerke, 2007). A podcast is defined in the New Oxford American Dictionary as ‘a digital recording of a radio broadcast or similar program – is typically made available on the web for downloading to a personal audio player’. Podcasting was the 2005 Word of the Year, according to the dictionary editors (McKean, 2005). Podcasts are digital files that can be downloaded and listened to whenever and wherever one wants (Barsky, 2006). Originally, podcasting referred to an audio file that was automatically delivered directly to the listener's device using the XML-based format RSS (Really Simple Syndication) and a feed reader. Rather than the listener having to remember to check for new audio files or tune in to a broadcast on schedule, the feed-reader software would automatically check for and download any new audio to the listener's device. Recently, podcasting has become synonymous with any audio or video file that listeners download and play on a digital player (Worcester and Barker, 2006). Podcasts in university education The portable audio device is no longer simply a medium for music or video entertainment; it now conveys a lot of educational material. Podcasting usage in education is increasing. With the potential to change the teaching and learning experience significantly, it can facilitate organization and delivery of information tailored to users’ individual preferences and learning styles (Harris and Park, 2008). Podcasts are asynchronous and allow for infinite review and reinforcement of the skills presented. Long files can be broken into smaller, more digestible chunks than typical instructional sessions in academia (Griffey, 2007).
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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 | Scholarly communication Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.015 |
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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".