ENT 530 Social Media Portfolio : Young Living Essential Oils By Laily / Nor Laili Ibrahim
Bibliographic record
Abstract
Young Living is a United States-based brand that uses the Seed to Seal technique to produce 100% natural essential oils. From the seed selection through the bottling of the essential oils, every step is carefully monitored and researched to ensure that no chemicals are contained. Young Living Essential Oils are also known as aromatherapy oils for skin, hair, and beauty. There are two types of membership programs which the first is Standard Membership that can only be purchased at the member price and does not need to be consistent. To get this membership, I need to buy Basic Starter Kit or Premium Basic Starter Kit. Second, Essential Reward Membership does not require membership fees and has many benefits. It just needs to buy essential oils only and there are 3 types of options to choose from. First, 100pv (point value) Membership; is equivalent to RM 560, second is Membership 150pv is equivalent to RM 760pv and the third is Membership 300pv and above is equivalent to RM 1500. Thus, Young Living Essential Oil by Laily was created in a way that required me to be a member of Young Living that under the guidance of Encik Muhammad Faharudin. I choose the Standard Membership program as the first step for me and since this is my first time venturing into the business, Young Living Essential Oil by Laily will concentrate solely only on essential oil products sales.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.435 | 0.281 |
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; the direct Gemma label and the distilled Codex classifier 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".