Bibliographic record
Abstract
If the advancement of technology and knowledge were to somehow freeze today, current students would have a very fair chance at leading successful careers and being positive contributors to societies of the future. For obvious reasons, that is not going to happen. Watching television for a handful of minutes or browsing the internet for a short while will more than likely alert the user to the reality of technology: it is ever-changing, endlessly advancing, and rapidly evolving beyond imagination. In light of this, teachers and leaders face one of the most important, yet one of the most challenging, tasks: how can educators effectively prepare learners for this continuous advancement in technology? Developing future-ready learners requires teachers and leadership teams to embrace changes in technology and hone their practices to reflect that. Ideally, educators must incorporate the teaching of skills and competencies related to the creation and use of technology, so that future generations have the tools to be successful contributors to their societies. Likewise, leaders must take full advantage of their influence in order move teacher practice forward. Maintaining a balance between being a catalyst of change and empathetic to the needs of others will likely result in positive changes towards making the use of technology a staple of every classroom. The shift in teaching and learning discussed in this paper is not simply the addition of gadgets to the classroom setting, nor is it a call for some written work to be typed, coding challenges to be completed sporadically, and the building of Lego Mindstorms as STEM projects. While these activities definitely involve the use of technology, the idea is for educators to build learner confidence and skills to be able to use any technology that becomes available, and to problem-solve and collaborate to create and invent within all the different realms of technology.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".