Bibliographic record
Abstract
Harry K. Wong , Rosemary T. Wong, (2009), The First Days of School – How to be an Effective Teacher, Singapore: CS Graphics Pte. Ltd. Ontario Public School Boards’ Association, (2009), What If? Technology in the 21 Century Classroom, Toronto: Leading Education’s Advocates Meg Ormiston, (2010), Creating a Digital-Rich Classroom – Teaching & learning in a Web 2.0 World, Bloomington: Solution Tree Press Mark Prensky, (2010), Teaching Digital Natives – Partnering for Real Learning, Thousand Oaks: Corwin Press Will Richardson, (2010), Blogs Wikis, Podcasts, and Other Powerful Web Tools for Classrooms, Third Edition, Thousand Oaks: Corwin Press Jeff Piontek, Blane Conklin, (2009), Blogs Wikis, Podcasts, Oh My!, Huntington Beach: Shell Education Mike Ribble, Gerald Bailey (2007), Digital Citizenship in Schools, Washington: ISTE Amy Benjamin, (2005), Differentiated Instruction Using Technology – A Guide for Middle and High School Teachers, Larchmont: Eye on Education Ontario Ministry of Education, (2010), Growing Success – Assessment , Evaluation, and Evaluation in Ontario Schools, Toronto: Queen’s Printer for Ontario William M. Ferriter, Adam Garry, (2010), Teaching the iGeneration – 5 Easy Ways to Introduce Essential Skills with Web 2.0 Tools, Bloomington: Solution Tree Press Ontario Ministry of Education, (2010), Student Success – Differentiated Instruction Educator Package, Toronto: Queen’s Printer for Ontario
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.517 | 0.468 |
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".