Creativity chains and playing in the crossfire on the video‐sharing site YouTube
Bibliographic record
Abstract
Purpose The purpose of this paper is to analyse how individual creativity of internet users is expressed in the production of online music videos and how the creative dynamic among amateur internet video producers can be characterized. Design/methodology/approach The researchers became readers and authors in the aim of providing the academic community with a scholarly narrative of creative YouTube video production. To develop their narrative, they explored the narrative woods that have grown up on the other side of the monitor screen in the form of videos inspired by one song. Findings The collective creative force is shown not to be expressed merely through the semantic and non‐semantic montages that make internet users into postmodern tinkerers, but also through such mechanisms as imitation, diversification and ornamentation. This force and these mechanisms give rise to chains that link and connect individual minds, imaginations, interests, enthusiasms, talents, abilities and skills. Practical implications As part of a relationship, or even a “conversation” to be initiated, sustained, and maintained on behalf of an industry organization, or brand with its consumers, the authors believe that the way to deal with digital participatory culture and the creative force manifested in innovation communities is to capitalize on these creative chains as judiciously as possible. Originality/value The authors suggest that this process should be part of a high‐impact interactive marketing strategy likely to promote (self‐) enchantment and foster loyalty among community members through (self‐) enchantment, particularly via the coproduction of a story, with community members creating the scripts.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".