Bibliographic record
Abstract
A local perspective iIt was September 29, 2016 and Marta Quelhas, head of the Personal Care Marketing Team of Unilever Portugal and Gonalo Bernardes, Marketing Director, were preparing for a conference call with the Global Marketing Team.The conversation was going to be about Dove Real Beauty Beats, a campaign that Unilever Portugal had launched 1 week earlier.Based on local research the Portuguese team had come to the conclusion that there were particular consumer insights that the Dove global campaigns were not addressing.Dove Beauty Beats video was designed as part of an Integrated Marketing Campaign to specifically target Portuguese consumers.First the video was launched using paid media, but soon it was clear that people were sharing and commenting and its spread did not need investment backing it up: it was reaching the entire planet organically.This conversation was expected to be challenging as traditionally the Global Office was responsible for developing master campaigns with no particular category or product and Local teams would then adapt it to local markets.Only one week had passed after the launch of the campaign and it was clear that results exceeded the expected outcome as the video had gone viral, crossed borders and it had gained a life on its own.Several countries, such as Canada, had contacted the Global Office asking to use Dove Beauty Beats so Marta and Gonalo needed to decide on the next steps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".