Bibliographic record
Abstract
‘‘Podcasting’’ is portmanteau word—a combination of iPod (the popular device for playing compressed audio files) and ‘‘broadcasting’’. Podcasting software permits users to subscribe to pre-recorded audio shows and to automatically have new shows downloaded to their portable music players as new podcasts become available. The key benefit of podcasting is time-shifting. Traditional radio works on the temporal model of having listeners tune in at the time programming is broadcast. In contrast, podcasting involves pre-recording audio broadcasts, uploading these ‘‘broadcasts’’ to the internet, and downloading these to MP3 players—permitting users to listen to content at a time that is most convenient to them. While podcasting is still largely the domain of hobbyists, podcasting has also been adopted, or trialed, by traditional terrestrial broadcasters, including the BBC, National Public Radio, and the Canadian Broadcasting Corporation. These traditional media companies are often using podcasting to provide added convenience to their audience and to further build their listener base. In addition, print media such as USA Today, Business Week and the Harvard Business Review are experimenting with podcasting. In these cases podcasts provide an alternative format toprintmedia—one thatmaybemore convenient for some listeners to consume. In the last year podcasting growth has been remarkable. This growth is obvious when we look at the number of podcasts hosted by just one source of podcasts, feedburner.com: On November 1, 2004 there were an estimated 212 podcasts on this service.1 By January 10, 2005 this number had reached 1,090. Over 1,000 new podcasts are appearing each month, with an estimated 5,302 podcasts on feedburner.com in May 2005. Consumer interest in podcasting is also growing. The Diffusion Group estimates that the US podcast audience will rise from 840,000 (in 2004) to 56 million by 2010.2 This growth will be fuelled by increased ownership of portable MP3 players. At present, around 22 million people in the United States own iPods or other MP3 players.3 Around six million of these people have downloaded podcasts and podcasting is expected to reach 12.3 million households by 2010.4 In late June, 2005 Apple Computer Inc added a podcasting feature to their iTunes software, making over 3,000 podcasts available for free.5 Apple are promoting podcasting with catchphrases such as ‘‘Radio Reborn’’ and ‘‘Podcasting. The next generation of radio’’.6 In addition to allowing users to download individual podcasts or subscribe to podcasts, iTunes also allows podcasts’ creators to publish their podcasts. Within two days over one million subscriptions to podcasts had been made through
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 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 | 0.021 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.028 | 0.021 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.021 | 0.027 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".