Bibliographic record
Abstract
There are three themes that permeate the education literature at the moment .One is the need for 21st century skills to equip youth with the necessary tools to succeed in the new millennium, which of course is well underway after 13 years.As currently articulated, this means knowing how to access content knowledge efficiently and effectively and to acquire inquiry/problemsolving skills that are meaningful, adaptable, and integrative.The second is the impor tance of developing creative, collaborative, communicative, and innovative learners who are culturally sensitive, globally aware, and who behave in ethically respon sible ways.The third is the need for developing digital literacy to keep pace with the exponentially burgeoning digital world that offers vast promise, but at the same time demands a critical stance to ensure that the power of these tools is used responsibly in what Gardner (2012) terms "good work."Technology is playing a critical role in how curricula are being developed and implemented.This is reflected in a huge movement in many countries to create STEM (science, technology, engineering, and mathematics) curricula to prepare students for lifelong learning and the demands of the future.Others have proposed that this acronym should be expanded to that of STEAM (science, technology, engineering , arts-language, visual and performing-and mathematics) if educators truly wish to embrace creativity and innovation in all its forms (Catchan, 2013).In addition, pedagogies are being re-thought as learning how to learn becomes paramount in inquiry learning and problem solving.An example is the trend towards the "flipped classroom" where "fact learning" is relegated to independent work on the part of the learners and frequently accessed electronically, the lecture-style of transmission learning is eliminated, and classrooms become hives of activity, exploration, application, discussion, reflection, and collaboration.There is no doubt that technology has helped to facilitate this, and to widen the possibilities for teaching learning and connection.At the same time it has created new problems around issues of accessibility, safety, and accountability.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.080 | 0.069 |
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".