Réussir son blog professionnel : Image, communication et influence à la portée de tous Ed. 2
Bibliographic record
Abstract
Affirmez votre presence en ligne et maitrisez votre communication : des choix strategiques a la gestion quotidienne, toutes les cles pour bloguer efficacement en toute serenite ! Definissez votre strategie de communication (ligne editoriale, public, theme, ressources...) Adaptez vos choix techniques en consequence (logiciel, extensions, widgets, hebergement...) Attirez clients et recruteurs en partageant votre expertise, renforcez votre identite ou communiquez avec vos collaborateurs Animez votre blog en echangeant avec vos lecteurs via les commentaires, blogrolls, pings et retroliens, dans le respect de la loi et de la netiquette Etendez votre reseau en integrant votre blog a l'ecosysteme des medias sociaux (fils RSS, Facebook, Twitter...) et ameliorez votre referencement Mesurez votre audience et votre popularite et controlez votre reputation numerique Monetisez votre blog pour un juste retour sur investissement Tirez parti des outils de publication a distance (pour Windows, Mac, iPhone et Linux) Blogueur professionnel confirme, le prefacier Philippe Martin, connu a travers son blog N'ayez pas peur !!, est co-auteur du livre Pourquoi bloguer dans un contexte d'affaires (2008), paru aux Editions Isabelle Quentin (Quebec).
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.010 |
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".