Bibliographic record
Abstract
What Should I Spend On Marketing?T he amount you spend on marketing is not an expense, like what you pay to lease your practice space-it's an investment.You're putting money into your business in order to grow it, gain patients, and increase profits.If you've invested in marketing before and didn't get the results you were hoping for, it could be that you weren't spending enough to make an impact, tracking was difficult or it was not set up properly (example: targeting for an digital ad campaign).Determining the ideal starting figure for your marketing investment will come down to what growth you want to see and importantly, where you're at right now. KNOW YOUR BIG NUMBERSMaybe you've previously been told to spend 5% of last year's sales or 7% of future sales.These figures might work in certain industries and situations but not all.A better starting point when it comes to an eye care practice is looking at your trailing 12 month revenue.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.094 | 0.063 |
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".