Bibliographic record
Abstract
Cet article soutient que la métaphore de la société du savoir est une transposition non critique de L'économie du savoir. On y examine trois approches différentes: L'idée d'un citoyen bien informé, les mesures institutionnelles et les attentes sociales pour être bien informé ainsi que le rôle d'Internet dans sa prestation de renforcements critiques d'une société du savoir. Ces trois approches se révèlent sérieusement déficientes. L'article suggGre qu'une culture peu encline au savoir serait une meilleure métaphore dans la mesure où les processus sociaux créant un haut degré de spécialisation du savoir en milieu de travail peuvent servir à accroître L'ignorance dans la société en general. This paper argues that the knowledge society metaphor is an uncritical transposition from the knowledge economy. It examines three different approaches to the former. These include the idea of the well‐informed citizen, the institutional arrangements and social expectations for being knowledgeable, and the role of the Internet in providing critical underpinnings of a knowledge society. All three approaches are found to be seriously deficient. The paper suggests that a knowledge‐aversive culture may be a better metaphor, as the social processes creating a high degree of knowledge specialization in the workplace may serve to increase ignorance in the broader society.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.013 | 0.076 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".