Bibliographic record
Abstract
It was a late afternoon in Cairo on December 6 th , 2010, when Ms. Nada Habib, Head of Marketing at Mayer Brothers (MB), was sitting in her office in Maadi, Cairo, looking at the latest share reports for Toilet Soap.Since 2008, the imported Emirati brand, Marina, had been growing continuously in shares on the expense of Mayer Brothers' brands, as well as its key competitor Tri-Star.Ms. Habib thought about how this brand had managed to grow and capture shares in such a stagnant market, with no apparent advertising or obvious consumer support.She had planned a marketing campaign for Venus-a beauty brand that was due for launch in the first quarter of the year 2011.Deep down however, she understood that this would not be a long term cure for her category.The Mayer Brothers board of directors asked her to develop a three-year plan to deliver sustainable, double digit growth.Accordingly, she needs to submit her plan by January, and as 2011 approaches quickly, she ponders on how to utilize her different brands and product ranges to win in this dynamic market, and stop competition from growing on her expense.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".