Unveiling videos: Consumer‐generated ads as qualitative inquiry
Bibliographic record
Abstract
Abstract Companies spend millions of dollars researching consumers, consumer attitudes to brands, and consumer uses of products. Yet the irony is that consumers are now doing this research themselves and posting their material to video‐sharing sites such as YouTube. In this paper we argue that the BASIC IDS framework (Cohen, 1999 ) for dimensional qualitative research can be used to deconstruct consumer‐generated videos to yield valuable insights into the paradoxes of consumer–service interactions. One category of service that has gained huge media attention of late, and yet is poorly understood, is the phenomenon of online social networks. Using three consumer‐generated ads about the social networking site Facebook, we explore the paradoxes of consumer–service interaction, namely consumers' ambivalent attitudes to the service, how the consumer uses and is used by the service, how the service both facilitates behavior and changes behavior, and how the service mediates social interactions yet drives social actors. Finally, we locate the findings in terms of the wider context of Gen Y and the digital revolution, specify limitations, and cite implications and avenues for future research. © 2011 Wiley Periodicals, Inc.
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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 | 0.062 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".