Bibliographic record
Abstract
My presentation focuses on the movement of information and knowledge to create social change. I explore whether Knowledge Mobilization (KMb) units could assist grassroots movements in sharing their goals and information with a broader audience. I do so through a textual analysis of a KMb unit social media and publications. Findings suggest that while a KMb does provide a variety of services, they are focused on supporting academics. Thus, it is unclear if connecting with a KMb unit would help a grassroots movement.Ma présentation porte sur le mouvement de l'information et du savoir pour créer un changement social. J'examine si les unités de mobilisation des connaissances (KMb) pourraient aider les mouvements locaux à partager leurs objectifs et leurs informations avec un public plus large. Je le fais à travers une analyse textuelle des médias sociaux et des publications d’une unité KMb. Les résultats suggèrent que bien que les KMb fournissent une variété de services, elles se concentrent sur le soutien aux universitaires. Ainsi, il n'est pas clair si la connexion avec une unité KMb aiderait un mouvement de base.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".