Bibliographic record
Abstract
As technology and its use continuously evolve, standards to inform practice become increasingly helpful. The International Society for Technology in Education (ISTE) has developed technology standards aimed at teachers, learners, and administrators, with the latest versions in 2016, 2017, and 2018 (International Society for Technology in Education, 2016, 2017, 2018). TESOL International Association developed the TESOL Technology Standards Framework , with versions for learners and for teachers, in 2008. This document is now freely available for non-commercial use, with attribution (CC BY SA NC). TESOL expanded the document into a book in 2011 with performance indicators, vignettes, and can-do statements for each standard. The 2011 standards themselves have been validated by recent research (though some of the performance indicators and vignettes have become dated. More recently, separate targeted technology standards were developed for Canadian adult language learners, teachers, and programs. (All three of the standards documents include the standards, performance indicators, reflection questions, and self-assessment checklists. The instructor and program standards also include vignettes. The standards are freely available for non-commercial use, with attribution (CC BY SA NC). Teachers and administrators looking for guidance about incorporating technology appropriately and effectively would be well served by exploring the TESOL International Association and Canadian standards.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.024 |
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".