Bibliographic record
Abstract
Small and medium enterprises (SMEs) have been adopting the internet at a feverish pace. Recent studies have shown that up to 85% of SMEs in industrialized countries have web sites, yet less than half are utilizing these web sites to securely transact with their customers. Consumer media consumption is moving away from traditional media, like newspapers, to the internet. These revelations coupled with the growth of tools and techniques available to support online marketing, make it a perfect time for SMEs to market their web sites and ultimately succeed online. In this chapter we will present and support the hypothesis that SMEs should stop investing in their web site’s design and functionality and start investing in efforts to market their web sites online, no matter how lousy their web site may be in comparison to today’s standards. With the support of two case studies, illustrating the successful utilization of internet marketing by two very different SMEs, we will relate how a SME can effectively market their web site online. We will also discuss the tools and techniques available to help an SME successfully begin a journey of internet marketing.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
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; 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".