Errors in Marketing Strategies & Services of Middle & Small Private Enterprises
Bibliographic record
Abstract
Net marketing is a new mar keting method. While there exist certain errors which have affected somewhat its development. We have to distinguish first of all those misunderstandings, then eliminate them for a better future development. Key words: middle & small enterprises, net marketing, errors and strategies Resume Le marketing sur internet est une nouvelle methodes de marketing. Lorsqu’il existe des erreurs qui ont influence leur processus de developpement. Il nous faut distinguer tout d’abord les malentendus, ensuite les eliminer pour un meilleur futur du developpement. Mots-cles : petites et moyennes entreprises, Le marketing sur internet, erreurs et strategies 摘 要 網絡營銷是一種新型的營銷手段，在實施的過程中，企業難免出現一些誤區，或多或少地影響了網絡營銷這種最新型、最有前景的營銷手段的發展。要幫助民營企業縮短漫長的摸索過程，首先需要消除的是民營企業對網絡營銷的一些誤解，然後再把其從誤區中引領出來，讓網絡營銷逐漸為越來越多的企業所認識，隨著網路逐漸的平民化，越來越多的中小型企業已經開始感覺到，是到了該試試網絡營銷的時候了。 關鍵詞：中小型企業；網路營銷；誤區對策；策劃服務
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".