Bibliographic record
Abstract
Le présent article explore le passé, le présent et l’avenir du marketing en bibliothèque aux États-Unis. Bien que les bases de celui-ci aient été posées il y a plus d’un siècle, les bibliothèques ne sont pas toutes à jour en matière de connaissances et de pratiques du marketing. Il y a celles qui tirent avantage d’une grande superficie, d’une équipe d’envergure et d’un budget en conséquence, et d’autres qui manquent de certaines nécessités. C’est dans ce paysage contrasté que l’auteure aborde la question des organisations et des publications qui appuient le marketing en bibliothèque, classe les tendances qui se dessinent en quatre catégories, puis dresse la liste des campagnes promotionnelles et des prix de reconnaissance à l’échelle nationale. Une attention particulière a été accordée aux défis constants qui se posent et aux futurs scénarios possibles.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.029 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".