Bibliographic record
Abstract
If there is one genre that has been transformed by the digital age, it is the music video. Many of today's most popular singers and groups have been discovered on sites such as YouTube. Canadian singer Justin Bieber became a superstar at the age of 15 on the basis of the songs he uploaded to the video-sharing site. Here, amateurs reign supreme: recording songs in their bedrooms one minute, and the next attracting corporate sponsors to pay for product placement in their clips or production of online adverts. Record a version of a Lady Gaga song at your college music festival and you may end up with 50 million views in a month, as did Greyson Chance (youtu.be/bxDlC7YV5is). But it is not only the discovery of new talent in terms of performance that has transformed the music video, but the arrival of fan-made videos. Billboard now recognize fan-made videos that use authorized audio, as well as official promos, in the compilation of charts of the most popular online music. It is yet another example of the omnipresence of ‘own-created’ media and how this is having an impact on, or indeed taking over, the mainstream. However, YouTube music videos can be used for many other purposes these days. For example, the song ‘Crush on Obama’ (youtu.be/wKsoXHYICqU), performed by a fan of the politician, then filmed and edited by amateurs and uploaded to YouTube, was said to have played a major part in the election of the US President in 2009. So, what are the implications of these enormous changes for the language classroom? Traditionally, music videos have been exploited in the language classroom by gap-fills based around the song's lyrics. Whilst this is a worthwhile exercise in many ways, and clearly tests students’ listening skills, it does not consider the role of the video itself but rather gives priority to the text in isolation. Such activities can be done equally well with audio alone. Secondly, there are a number of online tools and sites available now (a good example is lyricstraining.com) which enable learners to do these lyric gap-fills on their own outside class
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.304 | 0.127 |
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".