Bibliographic record
Abstract
This resource was created to embark on a STEM project in grade 8 science class. Students are exposed to four different units during the year in Ontario, including: Cells, Fluids, Systems in Action, and Water Systems. The learning objective was to create a project that linked each of these units together under a “systems” theme and incorporate engineering, math, and technology. Students were able to showcase their learning in a final presentation that highlighted the different components of the STEM project. This includes the pulley schematics, design, and calculations of their water filter and technology implementation. This project uses Google Apps for Education so students can collaborate on the project synchronously and asynchronously, as well as incorporates hard skills like using a microscope, and provides students with the opportunity to design and build techniques. Cette ressource a été créée pour s’embarquer en un projet de STIM en utilisant comme guide le curriculum de science ontarien de la 8e année. Les étudiants apprennent à propos quatre sujets différents pendant l’année scolaire, incluant Les Cellules, Les Fluides, Les Systèmes en Action, et les Systèmes Hydrographiques. L’objectif d’apprentissage était de créer un projet qui liait tous ces sujets ensemble sous un thème commun, les systèmes, et intégrerait aussi l’ingénie, les mathématiques, et la technologie. Les étudiants ont eu la chance de montrer les connaissances qu’ils ont apprises dans une présentation finale qui a souligné les éléments différents du projet. Cela inclut les moufles, la conception, les calculs de leur filtre à eau et l’implémentation de la technologie. Ce projet utilise ‘Google Apps for Education’ pour que les étudiants puissent collaborer sur le projet en synchronie et seule. Il incorpore aussi les compétences du niveau plus élevé, comme l’utilisation d’un microscope et donne les étudiants la chance de développer les stratégies et techniques de la conception et construction.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.247 | 0.100 |
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".