Bibliographic record
Abstract
In this period, known as the anthropocene, humans are having a profound influence on the planet, changing the atmosphere we breathe and reshaping the earth’s surface, thereby triggering species extinction at an alarming rate.Information Studies professionals and students can have a profound influence on the data that is collected, how it is stored, retrieved and communicated with citizens and communities. We have a responsibility to help to heal our planet by raising awareness and triggering action. This talk challenges researchers, practitioners, teachers and students to lead the way in shaping a sustainable future. We can change information processes and technology, raise awareness, and engage citizens to contribute to science and their own communities by becoming “citizen scientists.” À notre époque, connue sous le nom d’anthropocène, les activités humaines ont un impact profond sur la planète, elles modifient l'atmosphère que nous respirons et elles remodèlent la surface de la terre, provoquant ainsi l'extinction d’espèces à un rythme alarmant.Les étudiants et professionnels des sciences de l'information peuvent exercer une influence déterminante sur les données collectées ainsi que sur leur mode de stockage, d’extraction et de communication aux citoyens et aux communautés. Il est de notre responsabilité d'aider à la guérison de notre planète par des actions concrètes de sensibilisation. Cette conférence met les chercheurs, les praticiens, les enseignants et les étudiants devant le défi de montrer le chemin vers l'élaboration d'un avenir durable. Nous pouvons changer les processus et les technologies informationnelles, nous pouvons favoriser la prise de conscience et motiver les citoyens à contribuer à l’activité scientifique et à s’engager dans leurs propres communautés en devenant des «citoyens chercheurs».
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 | 0.151 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.017 | 0.059 |
| Scholarly communication | 0.055 | 0.112 |
| Open science | 0.008 | 0.031 |
| Research integrity | 0.030 | 0.032 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".